AI Engineering & Delivery Lead (12 – 15 years of Industry Experience) Location: Hyderabad with Hybrid work (with presence in Hyderabad) Type: Full Time / Perm Staff Shift: 2 pm – 11 pm IST, Mon thru Fri. We’re seeking an AI Engineering & Delivery Lead who can design, build, and ship production AI solutions on Azure—then partner with customers and field teams to land them. You’ll lead end-to-end model lifecycle on Azure Machine Learning and Azure AI Foundry (Azure AI Studio), orchestrate robust MLOps pipelines, and translate business goals into scalable architectures. You’re equally comfortable whiteboarding with executives, pairing with engineers, and tuning latency/cost for real-world workloads. Experience across other clouds (AWS, Google Cloud, Oracle) is a plus—we meet customers where they are. Responsibilities Solution Design, Engineering & Delivery > Own end to end delivery execution for Agentic AI solutions from Proof of Concept through Production, ensuring predictable, high quality outcomes. > Translate product scope and architectural direction into clear delivery plans, sprint cadence, and execution milestones. > Drive day to day engineering delivery, including dependency management, issue resolution, and removal of execution blockers > Lead and mentor a multidisciplinary engineering team ( AI Engineers, API Developers, Cloud Infra Engineers, DevOPs and QA) > Own reference architectures for classical ML and GenAI (RAG, fine-tuning, tool/use-case orchestration) on Azure ML + Azure AI Foundry. > Design secure, scalable MLOps with AML v2 (pipelines, components), GitHub Actions/Azure DevOps, model/feature registries, online/batch endpoints, and CI/CD. > Build data/feature pipelines using Fabric/Synapse/Databricks, Delta/Parquet, and govern with Purview; integrate Key Vault, Private Link, VNets, Managed Identity. > Productionize inference on Managed Online/Batch Endpoints or AKS; implement monitoring (drift, data quality, performance, cost) and A/B/Canary rollouts. GenAI & Apps > Implement Azure OpenAI / Azure AI model catalog patterns (Prompt Flow, safety filters, content moderation, grounding with vector search). > Deliver RAG systems (Azure Cognitive Search or vector DBs), retrieval evaluators, prompt/version management, and cost/latency optimization. Solution Engineering > Lead discovery, write Solution/Architecture Design Docs, demo/reference apps, and run customer workshops/POVs. > Partner with Sales/Customer Success; create estimates, landing zones, and handoffs to customer/managed services teams. Standards & Governance > Embed Responsible AI practices (privacy, safety, fairness, transparency), threat modeling, and compliance-by-design. > Establish coding standards, repo strategy, IaC (Bicep/Terraform), observability (App Insights/Log Analytics), and SRE runbooks. > Candidates with backend development experience in Java, .Net technologies prior to moving to AI are preferred. Requirements Required Qualifications > 12 – 15 years in data/ML/AI engineering with 3+ years building production solutions on Azure ML and Azure AI Foundry/Studio. > Led Enterprise-Grade AI project delivery with a team including a part of individual contribution > Delivered .Net / Java projects to large global clients (Domestic client projects are not qualified)) prior to delivering AI Projects > Proven delivery of ML/GenAI projects end-to-end: problem framing, data/feature engineering, modeling, evaluation, deployment, and monitoring. > Hands-on with: AML SDK v2 & pipelines, MLflow/Model Registry, Feature Store, Managed Endpoints/AKS, Prompt Flow, GitHub Actions/Azure DevOps. > Strong Python engineering (PyTorch/Transformers or scikit-learn/lightGBM), containerization (Docker), and API design (FastAPI). > Data platforms: Fabric/Synapse/Databricks; storage (ADLS, Delta); messaging/streaming (Event Hub/Kafka) fundamentals. > Security & networking on Azure: Key Vault, Private Link, VNet, Managed Identity, RBAC. > Executive-level communication; ability to lead architecture reviews and mentor engineers. Education: > UG or PG in one of the streams: Computer Science Engineering / Data Science / Statistics Prior Employer Industry Background: > Strictly within IT Services industry ( Product based experience will not qualify for this role) Preferred / Nice to Have > Cross-cloud exposure: AWS SageMaker, Google Vertex AI, Oracle OCI Data Science / Generative AI; portability patterns across providers. > Vector databases (Azure AI Search vector, Pinecone, Redis, pgvector), LlamaIndex/LangChain, evaluation frameworks (Ragas, Promptflow eval). > Databricks (Unity Catalog, Feature Store), Power BI/Fabric Real-Time Intelligence, or Snowflake/Mosaic AI familiarity. > IaC (Terraform/Bicep), Kubernetes (AKS), GPU workload tuning, Triton/ONNX, quantization/LoRA/SFT pipelines. > Certifications: Azure AI Engineer/Architect; AWS/GCP/Oracle equivalents. How You’ll Measure Success > Production launches with measurable business impact (quality, latency, reliability, cost). > Reusable assets: reference architectures, accelerators, and well-documented repos customers adopt. > Clear governance & Responsible AI controls; zero critical security findings in reviews. > Field enablement: workshops/POVs that convert to deployments.