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IT Staffing & Recruitment

Scale Your Team with Vetted AI & Data Talent

Access pre-assessed machine learning, data and engineering specialists through contract, contract-to-hire, dedicated team and C2C/offshore models.

Why Adivi for staffing

Practitioners first. Recruiters second.

Adivi is not a staffing agency that picked up some AI keywords. We build AI, data and application systems ourselves, so we assess candidates the way your engineering leaders would: on architecture judgement, production experience and delivery discipline.

Every shortlist is reviewed by people who have shipped the kind of systems you are hiring for. That is why our profiles hold up in technical interviews and our placements get up to speed faster.

14
specialist role families
4
regions served
4–25
years' experience per profile

Roles we staff

Specialists for the teams AI and software programmes depend on.

Solution Architecture

Architects who align systems, data and delivery around measurable outcomes.

AI Team Leadership

Hands-on leads who own architecture decisions and mentor engineering teams.

GenAI / LLM Engineering

Builders of RAG systems, agentic workflows and domain-specific AI assistants.

Computer Vision & Edge AI

Vision and inference engineers for real-time video analytics and edge deployment.

Forward Deployed Engineering

Customer-facing engineers who embed with your team and get solutions live in your environment.

ML Engineering

Engineers who design, train and productionise machine learning systems for real business decisions.

Data Science

Scientists who turn complex data into models, experiments and decisions leaders can act on.

MLOps & AI Platform

Specialists who automate deployment, monitoring and reliability for models in production.

Data Engineering

Pipeline and platform engineers who make analytics and AI dependable at scale.

Data & Business Analytics

Analysts who build the reporting, dashboards and metric layer decisions are made on.

NVIDIA & Accelerated Computing

CUDA, TensorRT and Triton developers who make GPU workloads fast and cost-efficient.

Full-Stack Engineering

Product engineers who ship complete web applications, APIs and front ends.

Application Development

Enterprise application engineers for backend services, integrations and modernisation.

Project & Product Management

Delivery and product leaders who keep scope, roadmap and stakeholders aligned.

Available profiles

Profiles matched to your requirement, not to an industry.

These are the profiles we staff most often. Each one comes with proven experience shipping production systems, and each works across sectors — so you can hire on engineering depth rather than a narrow domain label. Share your requirement and we'll match the right profile. Open a profile to see its full skills and what it can deliver.

Solution Architect – AI6+ yearsArchitects who set the technical direction for multi-agent, RAG and knowledge-graph platforms, including in regulated environments.Multi-agent systemsLangGraph / CrewAIGraphRAG & Neo4j

Core skills

Architecture & leadership
End-to-end technical strategysolution design documentsdeployment lifecycleleading teams of 3–7 engineerscost and capacity planning
GenAI & orchestration
RAG architecturesCrewAILangGraphLangChainMCP (FastMCP)multi-agent pipelineshuman-in-the-loop designprompt and context strategy
Graph & knowledge
Neo4j & Graph Data ScienceCypherGraphRAGontology and knowledge-graph designAmazon NeptuneRDF / SPARQL (Neo4j Certified Professional)
APIs & ML
DjangoFastAPIRESTGraphQLgRPCTensorFlowPyTorchYOLOXGBoostOpenCV
Data
PostgreSQLMySQLMSSQLOracleOpenSearchElasticsearchSnowflakeDatabricksBigQueryRedshiftS3Azure BlobDelta Lake
Cloud & compliance
AWSAzureGCPDockerKubernetesTerraformCI/CDmulti-cloud and hybrid architecturesecrets managementRBAC / SSOLLM audit loggingHIPAAGDPRSOC 2 and ISO 27001 aligned design

What this profile can deliver

  • Design multi-agent workflows with human-in-the-loop validation for approval-heavy and regulated processes.
  • Architect multilingual RAG assistants with high contextual relevance and measurable answer quality.
  • Build GraphRAG platforms that answer natural-language questions over enterprise data schemas.
  • Design secure, low-latency multi-tenant sandboxes for autonomous agent code execution.
  • Apply compliance-aligned architecture patterns, including audit logging for every LLM call.

Also fits: AI Architect, Enterprise Architect, Cloud Solution Architect, Data & AI Architect

AI Team Lead / Engineering Lead8+ yearsHands-on leads who own architecture decisions, run AI delivery and mentor the engineers building the systems.Team leadershipAI delivery ownershipArchitecture decisions

Core skills

Leadership & delivery
Leading teams of 3–10 engineerssprint and release planningestimationtechnical roadmapshiring and technical screeningmentoring and code reviewstakeholder communicationAgile / Scrum / Kanban
Architecture
End-to-end AI system designbuild-vs-buy decisionsmodel selectioncost and latency budgetingsecurity and access designtechnical documentation and ADRs
GenAI & agents
LLMsRAGMCPLangChainLangGraphCrewAIAmazon BedrockAzure OpenAIVertex AIevaluation with RAGAS and Langfuse
ML / DL
TensorFlowPyTorchHugging FaceScikit-learnXGBoostMLflowexperiment tracking
Data
PythonSQLPySparkPostgreSQLSnowflakeDatabricksBigQueryRedshiftvector databases
Cloud & DevOps
AWSAzureGCPDockerKubernetesCI/CD (GitHub Actions, Jenkins, Azure DevOps)observability and tracing

What this profile can deliver

  • Own an AI engineering workstream end to end, from architecture through production release and handover.
  • Set engineering standards: review cadence, testing, evaluation gates and release criteria for AI features.
  • Translate business goals into a sequenced technical roadmap with realistic scope and staffing.
  • Grow the team through structured mentoring, pairing and technical screening of new hires.

Also fits: Lead AI Engineer, Engineering Manager – AI, Technical Lead, Head of AI Delivery

Senior AI Engineer (GenAI / LLM)6+ yearsHands-on engineers who take generative AI, RAG and agentic systems from prototype to production in any domain.LLMs & RAGMCP / Agentic AILangChain / LangGraph

Core skills

GenAI & agents
LLMs (GPT, Claude, Gemini, LLaMA, Mistral, Qwen)RAGprompt engineeringfine-tuning (LoRA, QLoRA, PEFT)Model Context Protocol (MCP)LangChainLangGraphLlamaIndexCrewAIAutoGenSemantic Kernelfunction callingguardrails
Model platforms
Amazon BedrockAzure OpenAIGoogle Vertex AIOpenAI APIAnthropic APIHugging FaceOllamavLLMNVIDIA NIM
Evaluation & observability
RAGASDeepEvalLangSmithLangfuseTruLensprompt regression testinggrounding and hallucination checks
ML / DL
TensorFlowPyTorchHugging Face TransformersScikit-learnspaCyOpenCV
Data & vector stores
PandasPySparkPostgreSQL (pgvector)MySQLMongoDBDynamoDBSnowflakeDatabricksRedisChromaDBFAISSPineconeWeaviateQdrantMilvusOpenSearchElasticsearch
APIs & backend
PythonFastAPIFlaskDjangoNode.jsRESTGraphQLWebSocketsasync workers (Celery, SQS)
Cloud & DevOps
AWSAzureGCPDockerKubernetesTerraformGitHub ActionsGitLab CIJenkins

What this profile can deliver

  • Build RAG document Q&A and knowledge assistants with hierarchical chunking, hybrid search, re-ranking and citation grounding.
  • Design agentic workflow automation with reusable MCP tool servers for reporting, data transformation and communication.
  • Ship production LLM services with caching, streaming, cost controls, guardrails and continuous evaluation.
  • Integrate generative AI into existing products and enterprise systems without disrupting delivery.

Also fits: Generative AI Engineer, LLM Engineer, RAG Engineer, NLP Engineer, AI Application Developer

Computer Vision & Edge AI Developer6+ yearsVision and inference specialists for real-time video analytics and AI running on edge devices.NVIDIA TritonDeepStreamTensorRT

Core skills

Computer vision
PyTorchTensorFlowOpenCVYOLO (v5–v11)Detectron2MMDetectionSAMVision TransformersResNetEfficientNetOCR (Tesseract, PaddleOCR, TrOCR)pose estimationsegmentationtracking (DeepSORT, ByteTrack)
GPU & edge inference
NVIDIA Triton Inference ServerDeepStreamDALITensorRTONNX RuntimeOpenVINOTensorFlow LiteCoreMLJetson Orin / Xavier / NanoRaspberry PiCoral TPU
Optimisation
INT8 / FP16 quantisationpruningdistillationmulti-camera RTSP and GStreamer pipelinesframe batchinghardware-accelerated decode
Data & training
Annotation workflows (CVAT, Label Studio, Roboflow)augmentation (Albumentations)synthetic dataactive learningMLflowWeights & Biases
Deployment & observability
DockerKubernetesMQTT and Kafka event streamsGPU / VRAMFPS and latency telemetryPrometheusGrafanaAWSAzureGCP

What this profile can deliver

  • Scale a single-stream video prototype into a distributed, multi-camera analytics platform.
  • Quantise and optimise models to hit strict latency targets on edge hardware.
  • Build real-time RTSP video pipelines with DeepStream, GStreamer and Triton.
  • Instrument inference performance so throughput and cost stay visible in production.

Also fits: Computer Vision Engineer, Edge AI Engineer, Deep Learning Engineer, Video Analytics Engineer

Forward Deployed Engineer5+ yearsCustomer-facing engineers who embed with your team, adapt the product to real workflows and get it live in your environment.Python & APIsLLM app integrationCustomer-facing delivery

Core skills

Engineering
PythonTypeScript / JavaScriptSQLBashGoFastAPIFlaskNode.jsReactRESTGraphQLgRPCwebhooksSDK and connector developmentscripting and automation
AI integration
LLMs (GPT, Claude, Gemini, LLaMA)RAGprompt engineeringMCPLangChainLangGraphLlamaIndexembeddings and vector search (pgvector, Pinecone, FAISS, Qdrant, Weaviate)evaluation and guardrailsagent tool integration
Data & systems integration
PostgreSQLMySQLMSSQLMongoDBSnowflakeDatabricksBigQueryRedshiftKafkaAirflowETL / ELTCSV and file ingestionERP / CRM / ticketing / EHR system connectorsSSO (OAuth2, SAML, Okta, Entra ID)
Deployment & operations
DockerKubernetesTerraformAWSAzureGCPon-premise and VPC-isolated deploymentair-gapped installsCI/CD (GitHub Actions, GitLab CI, Azure DevOps)PrometheusGrafanaDatadogSentrylog triage and incident response
Customer-facing skills
Discovery workshopstechnical scoping and solution designlive demos and POCspilot-to-production planningrunbooks and enablement docstraining and handoverescalation managementfeedback loop into product roadmap
Security & compliance
RBACsecrets managementdata residencyPII handlingaudit loggingSOC 2GDPR and HIPAA aware deploymentssecurity questionnaire support

What this profile can deliver

  • Embed with a client team, learn the real workflow and adapt the product to it instead of forcing a process change.
  • Take a pilot from demo to production in the customer's own cloud, VPC or on-premise environment.
  • Build the custom connectors, data mappings and glue code an integration needs to actually go live.
  • Debug live issues in unfamiliar customer environments and turn recurring fixes into product improvements.

Also fits: Solutions Engineer, Implementation Engineer, Customer-Facing AI Engineer, Deployment Engineer, Technical Consultant

Senior ML Engineer – Predictive Analytics5+ yearsSenior ML engineers for predictive analytics at scale and AI automation of data-heavy operational workflows.Predictive modellingPySparkRecommendation systems

Core skills

Languages
PythonSQLR
Data processing
PySparkDatabricksPandasNumPyPolarsdistributed processingETL / ELT pipelinesAirflowdbt
ML / AI
Predictive modellingensembles (XGBoost, LightGBM, CatBoost, Random Forest)recommendation systemsclustering and segmentationsurvival analysisfeature engineeringstatistics and experiment design
Generative AI
RAGLLM integrationagentic systems (MCP, LangChain, LangGraph, Langfuse, RAGAS)document and audio-to-structured-data extraction
MLOps & platforms
FastAPIFlaskMLflowSHAPmodel registriesdrift and performance monitoringPostgreSQLMySQLMongoDBSnowflakeBigQueryRedshiftOpenSearch
Cloud & DevOps
AWS (SageMaker, Lambda, S3)Azure (ML, Data Factory)GCP (Vertex AI, BigQuery)DockerKubernetesGitHub Actions

What this profile can deliver

  • Build enterprise demand forecasting across thousands of SKUs or entities with Prophet and LightGBM.
  • Automate document- and data-heavy workflows with agents, including extraction and system-of-record integration.
  • Build matching engines that combine geospatial scoring, skill matching and workload balancing.
  • Deliver churn, segmentation and lifetime-value analytics on distributed Spark pipelines.

Also fits: Machine Learning Engineer, Data Scientist, Applied ML Engineer, Decision Science Engineer

ML Engineer – Forecasting, RAG & Agentic AI4+ yearsML engineers who combine forecasting and classical ML with generative AI, RAG and agentic pipelines.Time-series forecastingXGBoost / LightGBMRAG & LangChain

Core skills

Languages
PythonSQLRC/C++Scala
Forecasting
ARIMA / SARIMAXProphetNeuralProphetLSTMTemporal Fusion TransformerXGBoostLightGBMCatBooststatsmodelsDartsrolling-origin backtestinguncertainty quantificationhierarchical reconciliation
ML / DL
Scikit-learnPyTorchTensorFlow / Kerasanomaly detectioncausal inferencerecommendation systemsoptimisationA/B testing
Generative AI
LLMs (GPT, Claude, Gemini, LLaMA, Mistral)RAGLangChainLlamaIndexLangGraphagentic pipelinesfine-tuning (LoRA / PEFT)embeddings
Data & vector stores
PandasNumPyPySparkDatabricksSnowflakeBigQueryPostgreSQLMongoDBOpenSearchElasticsearchFAISSPineconeWeaviateQdrantChromaDB
MLOps & cloud
FastAPIMLflowWeights & BiasesDVCfeature storesdrift monitoringSHAPAWS (SageMaker, Bedrock, Lambda)Azure MLGCP Vertex AIDockerKubernetesAirflow

What this profile can deliver

  • Build demand and price forecasting systems with statistical and ML ensembles, backtesting and drift monitoring.
  • Add agentic AI to operational platforms: trend analysis, ranking, recommendations and conversational data assistants.
  • Build citation-grounded RAG research assistants with multi-turn memory.
  • Build privacy-first, on-device LLM pipelines that structure sensitive documents.

Also fits: AI Engineer, Machine Learning Engineer, Applied Scientist, NLP Engineer

MLOps / AI Platform Engineer5+ yearsPlatform engineers who automate training, deployment, monitoring and rollback so models stay reliable in production.KubernetesMLflow / KubeflowCI/CD

Core skills

ML lifecycle
MLflowKubeflowMetaflowSageMaker PipelinesAzure ML PipelinesVertex AI PipelinesDVCmodel registryexperiment trackingreproducible trainingfeature stores (Feast, Tecton)
Serving & inference
KServeSeldon CoreBentoMLNVIDIA TritonTorchServevLLMTGIRay ServeFastAPIautoscalingcanary and blue-green releasesA/B and shadow deployments
LLMOps
Prompt and model versioningevaluation gates (RAGAS, DeepEval)Langfuse and LangSmith tracingtoken and cost trackingrate limitingguardrailscaching
Infrastructure
DockerKubernetes (EKS, AKS, GKE)HelmTerraformPulumiAnsibleGPU schedulingRaySpark on Kubernetes
CI/CD & automation
GitHub ActionsGitLab CIJenkinsArgo CDArgo WorkflowsAzure DevOpsAirflowDagsterPrefectGitOps
Monitoring & reliability
PrometheusGrafanaELK / OpenSearchDatadogCloudWatchEvidentlyWhyLabsdata and concept drift detectionalertingSLOsincident response
Cloud & security
AWSAzureGCPIAM and RBACsecrets management (Vault, KMS)private networkingcost optimisationSOC 2 and ISO 27001 aligned controls

What this profile can deliver

  • Stand up an end-to-end ML platform: training pipelines, registry, automated deployment and rollback.
  • Take notebook-stage models into versioned, tested, monitored services with clear ownership.
  • Build LLMOps pipelines with evaluation gates, tracing and per-feature cost visibility.
  • Cut infrastructure spend through right-sizing, autoscaling and GPU scheduling without hurting latency.

Also fits: ML Platform Engineer, DevOps Engineer – ML, LLMOps Engineer, SRE – AI Systems

Senior Data Engineer – Data Platform & Pipelines7+ yearsData engineers who build the reliable pipelines, warehouses and lakehouses that analytics and AI depend on.Snowflake / DatabricksApache SparkAirflow

Core skills

Languages
SQLT-SQLPL/SQLPythonScalaPySpark
ETL / ELT
AWS GlueAzure Data FactorydbtPentahoInformaticaTalendSSISFivetranKafkaDebeziumchange data capture
Warehouse & lakehouse
SnowflakeDatabricksDelta LakeApache IcebergBigQueryAmazon RedshiftAzure SynapseApache SparkApache AirflowDagsterPrefectHivePresto / Trino
Modelling
Dimensional modelling (star and snowflake schema)Data Vaultslowly changing dimensionsmedallion architecturepartitioning and clusteringquery and cost tuning
Visualisation
Power BITableauQlik SenseQlikViewLookerSupersetDAX
Cloud & CI/CD
AWS (EC2, S3, RDS, VPC, Glue, EMR, Lambda)Azure (Data Factory, Synapse, Databricks)GCP (BigQuery, Dataflow)TerraformJenkinsGitHub ActionsDocker
Data quality & governance
Great Expectationsdbt testsvalidation frameworksautomated error detectionreconciliationlineage (OpenLineage, Purview, Collibra)GDPR-aware handling

What this profile can deliver

  • Build multi-source integration pipelines into Snowflake or Databricks with sub-24-hour refresh cycles.
  • Add validation and reconciliation checkpoints that catch data issues before they reach dashboards.
  • Deliver BI platforms with incremental reloads and clearly modelled, documented datasets.
  • Orchestrate ETL as Airflow DAGs and expose curated datasets through API and semantic layers.

Also fits: Analytics Engineer, Big Data Engineer, ETL Developer, Data Platform Engineer, BI Engineer

Data Analyst / Analytics Consultant4+ yearsAnalysts who turn raw data into trusted metrics, dashboards and recommendations decision-makers actually use.SQLPower BI / TableauPython & Excel

Core skills

Languages & tools
SQLPython (Pandas, NumPy, Matplotlib, Seaborn, Plotly)Radvanced Excel (Power Query, pivot tables, VBA)Google Sheets
BI & visualisation
Power BI (DAX, Power Query)TableauQlik SenseLookerLooker StudioSupersetMetabasedashboard and report design
Analytics
KPI and metric definitioncohort and funnel analysisretention and churn analysissegmentationRFMforecasting fundamentalsA/B testing and statistical significanceroot-cause analysisstorytelling with data
Data platforms
SnowflakeDatabricksBigQueryRedshiftAzure SynapsePostgreSQLMySQLMSSQLdbtAirflowsemantic layers
Product & marketing analytics
Google Analytics 4MixpanelAmplitudeSegmentAdobe Analyticsattribution modellingcampaign and funnel reporting
Governance
Data quality checksmetric documentationsingle-source-of-truth definitionsstakeholder training and enablement

What this profile can deliver

  • Replace ad-hoc spreadsheets with governed dashboards and a documented metric layer.
  • Run cohort, funnel and retention analyses that explain why a number moved, not just that it did.
  • Design and read A/B tests so product and marketing decisions rest on real significance.
  • Partner with data engineering to turn recurring questions into reusable, trusted datasets.

Also fits: Business Intelligence Analyst, Reporting Analyst, Product Analyst, Insights Analyst, Analytics Engineer

NVIDIA / CUDA Developer – Accelerated Computing5+ yearsGPU specialists who write and tune CUDA kernels, optimise inference and get the most throughput per GPU dollar.CUDATensorRTTriton Inference Server

Core skills

CUDA & kernels
CUDA C/C++CUDA Pythonkernel optimisationshared memory and warp-level primitivesstreams and concurrencycustom operatorsThrustCUBOpenCLTriton (OpenAI) kernels
NVIDIA stack
TensorRTTensorRT-LLMTriton Inference ServerNIMDeepStreamDALIcuDNNcuBLASRAPIDS (cuDF, cuML)NeMoIsaacOmniverse
Inference optimisation
INT8 / FP16 / FP8 quantisationpruningdistillationONNX and ONNX Runtimetorch.compilevLLMTGIbatching and KV-cache tuninglatency and throughput budgeting
Distributed & HPC
Multi-GPU and multi-node trainingNCCLDeepSpeedFSDPHorovodMPISlurmA100 / H100 / L40SMIG partitioning
Profiling & telemetry
Nsight SystemsNsight ComputenvprofDCGMGPU and VRAM telemetryFPS and latency trackingPrometheusGrafana
Frameworks & platforms
PyTorchTensorFlowJAXHugging FaceDockerKubernetes GPU operatorsAWS (EC2 GPU, SageMaker)Azure MLGCP Vertex AI

What this profile can deliver

  • Cut inference latency and cost by quantising, compiling and serving models with TensorRT and Triton.
  • Write and profile custom CUDA kernels where off-the-shelf operators leave performance on the table.
  • Scale training across multiple GPUs and nodes with NCCL, DeepSpeed or FSDP.
  • Right-size GPU fleets by instrumenting utilisation, throughput and cost per request.

Also fits: GPU Engineer, CUDA Developer, HPC Engineer, Inference Optimisation Engineer, Performance Engineer

Full-Stack Developer5+ yearsProduct engineers who ship complete web applications end to end, from database and API through to a polished front end.React / Next.jsNode.js & PythonTypeScript

Core skills

Frontend
ReactNext.jsTypeScriptJavaScript (ES6+)AngularVue.jsReduxZustandReact QueryTailwind CSSMaterial UIBootstrapHTML5CSS3SASSresponsive designaccessibility (WCAG)JestReact Testing LibraryCypressPlaywright
Backend
Node.jsExpressNestJSPython (Django, FastAPI, Flask)Java (Spring Boot).NET CoreC#PHP (Laravel)Ruby on RailsGoRESTGraphQLgRPCWebSocketsauthentication (JWT, OAuth2, SSO)
Databases
PostgreSQLMySQLMSSQLMongoDBRedisDynamoDBPrismaTypeORMSequelizeMongoosequery optimisationmigrations
Architecture
Microservicesserverlessevent-driven designmessage queues (Kafka, RabbitMQ, SQS)caching strategiesmulti-tenant SaaSAPI versioning
Cloud & DevOps
AWS (EC2, S3, Lambda, ECS, RDS, CloudFront)AzureGCPVercelDockerKubernetesTerraformGitHub ActionsGitLab CIJenkinsmonitoring (Sentry, Datadog, CloudWatch)
Practice
Agile / ScrumGitcode reviewTDDclean architectureperformance profilingCore Web VitalsSEO fundamentals

What this profile can deliver

  • Take a product from empty repository to deployed, monitored application with CI/CD in place.
  • Rebuild slow or legacy front ends into fast, accessible, component-driven interfaces.
  • Design and document APIs that internal teams and external partners can build against.
  • Integrate AI features — chat, search, recommendations — into existing product surfaces.

Also fits: Frontend Developer, Backend Developer, MERN / MEAN Developer, Web Application Engineer, Software Engineer

Application Developer – Enterprise Systems5+ yearsApplication engineers who build, integrate and modernise the business systems an organisation runs on.Java / .NET / PythonSpring BootAPI integration

Core skills

Languages & frameworks
Java (Spring Boot, Hibernate)C# / .NET Core.NET FrameworkPython (Django, FastAPI)Node.jsTypeScriptPHPVB.NETKotlin
Application architecture
Layered and hexagonal architecturemicroservicesmonolith-to-service decompositiondesign patternsSOLIDdomain-driven designbackground jobs and schedulers
Integration
RESTSOAPGraphQLgRPCwebhooksmessage queues (Kafka, RabbitMQ, Azure Service Bus, SQS)ESB and middlewareMuleSoftBoomifile and EDI interfacesERP / CRM / HRMS / payment API integration
Databases
MSSQLOraclePostgreSQLMySQLMongoDBstored proceduresT-SQLPL/SQLperformance tuningschema migration
Frontend & reporting
ReactAngularBlazorRazorJSPSSRSCrystal ReportsPower BI embedded
Cloud & DevOps
Azure (App Service, Functions, DevOps)AWS (ECS, Lambda, Elastic Beanstalk)GCPDockerKubernetesCI/CDIISLinuxGit
Quality & practice
Unit and integration testing (JUnit, xUnit, NUnit, pytest)SonarQubecode reviewAgile / Scrumsupport handovertechnical documentation

What this profile can deliver

  • Build line-of-business applications that fit existing processes rather than forcing a rewrite.
  • Integrate ERP, CRM and internal systems with resilient, well-monitored interfaces.
  • Modernise legacy applications incrementally — strangler pattern, API-first, cloud migration.
  • Take over and stabilise an inherited codebase: tests, documentation and a repeatable release process.

Also fits: Software Engineer, Backend Developer, .NET Developer, Java Developer, Integration Developer

Project Manager / Product Manager6+ yearsDelivery and product leaders who keep scope, roadmap, budget and stakeholders aligned from kickoff to launch.Agile & ScrumRoadmappingStakeholder management

Core skills

Delivery management
AgileScrumKanbanSAFeWaterfall and hybrid deliverysprint planningrelease managementcapacity planningestimationRAID and risk logsdependency managementsteering-committee reporting
Product management
Product strategy and roadmappingdiscovery and user researchbacklog ownership and prioritisation (RICE, MoSCoW, Kano)MVP definitionPRDsgo-to-market planningKPI and OKR definitionA/B testing and feature analytics
Commercial
Budget and forecast ownershipvendor and contractor managementSOW and change-order handlingresource allocationROI and business-case development
Tools
JiraConfluenceAzure DevOpsAsanaMonday.comClickUpTrelloMS ProjectSmartsheetMiroFigmaNotionSlackPower BI dashboards
Technical fluency
Software delivery lifecycleAPIs and integrationscloud delivery on AWSAzure and GCPdata and AI project lifecyclesCI/CD and release trainssecurity and compliance checkpoints
Certifications aligned
PMPPRINCE2CSM / PSMSAFe AgilistCSPO

What this profile can deliver

  • Run multi-team delivery with a realistic plan, visible dependencies and no surprise slippage.
  • Own a product roadmap grounded in user evidence and measurable outcomes, not feature wish lists.
  • Keep executives, engineers and vendors aligned with a clear, honest reporting cadence.
  • Recover an at-risk programme: re-baseline scope, rebuild trust and restore predictable delivery.

Also fits: Delivery Manager, Scrum Master, Technical Program Manager, Product Owner, Engagement Manager

Full-spectrum technology talent

Classical forecasting and BI through generative AI, agentic systems, computer vision, edge AI, full-stack and enterprise application development.

Industry-agnostic profiles

Our specialists work across sectors and adapt to your domain, so you hire for engineering depth rather than a narrow industry label.

Architecture to deployment

Profiles experienced in taking systems from prototype through CI/CD to production on AWS, Azure and GCP.

Flexible engagement

Staff augmentation, embedded pods or fixed-scope consulting delivery.

Engagement models

Structured to fit your operating model.

Contract

Individual specialists placed for a defined scope or period, ready to contribute from week one.

Contract-to-Hire

Evaluate a consultant inside your team before converting them to a permanent hire.

Dedicated Team

A standing, managed team built around your roadmap and delivery cadence.

C2C / Offshore

Corp-to-corp and offshore models that balance cost, coverage and time zones.

How it works

From requirement to onboarded specialist.

01

Submit your requirement

Share the role, skills matrix, delivery context and timeline.

02

We shortlist from our bench

We match vetted, pre-assessed specialists from our talent network.

03

You interview

Meet shortlisted candidates on your schedule. Technical depth is verified in advance.

04

Onboard

We handle engagement logistics so your new specialist starts contributing fast.

Geographies served

Wherever your teams operate.

Adivi places specialist technology talent across four regions, with engagement models that follow local contracting norms and cover your time zones.

United States
Europe
Australia / New Zealand
Middle East

Featured case study

Scaling candidate outreach with an AI voice agent

How we helped a staffing and recruitment business run natural, configurable screening calls at scale, across many roles and clients at once.

Read the case study →

Request talent

Tell us the role. We'll bring the shortlist.

Share your requirement and we'll start with a focused conversation about skills, timelines and the right engagement model. Interested in a specific profile? We'll arrange a technical screen and share matching resumes under NDA.

Ready to scale your team?

One conversation covers your requirement, our bench and the fastest path to an onboarded specialist.

Book an Appointment