Role & responsibilities

- Design, build, and ship agentic AI applications on Azure: multi-step reasoning, tool and function calling, retrieval, memory, and human-in-the-loop checkpoints.

- Implement retrieval-augmented generation pipelines, including chunking strategy, embedding and indexing, hybrid and semantic search, reranking, and grounding with citations.

- Integrate agents with client systems through APIs, databases, and Model Context Protocol (MCP) servers, writing the tool definitions and schemas the model depends on.

- Build evaluation harnesses and golden datasets, and treat accuracy, groundedness, and task completion as measured numbers rather than impressions.

- Instrument agents for production: tracing, token and cost telemetry, latency budgets, failure and fallback paths, and alerting on quality regressions.

- Implement guardrails and responsible AI controls, covering prompt injection defense, content filtering, PII handling, output validation, and clear boundaries on what an agent is allowed to do without human approval.

- Tune cost and latency through model selection, prompt caching, context management, batching, and routing simple work to smaller models.

- Apply engineering discipline to AI code: version control, code review, automated testing, CI/CD, and prompt and model versioning.

- Containerize and deploy agent services, owning the build, release, scaling, and runtime configuration of what you ship.

- Set technical direction on client engagements and mentor junior engineers, establishing patterns and standards the wider team can follow.

- Work directly with clients to turn ambiguous business problems into scoped agent use cases, and be honest about what current models can and cannot do reliably.

- Partner with Project Managers,



data engineers, and application teams to deliver end to end, and document what you build so others can operate it.

Preferred candidate profile

- At least 7 years in a software, data, or ML engineering role, with a minimum of 2 years building LLM-based or agentic applications that reached real users.

- Strong hands-on Python development, with real testing, packaging, and code review practice, not scripting alone.

- Practical experience with LLM APIs and agent frameworks, such as Microsoft Agent Framework, Azure AI Foundry, Azure OpenAI, or LangGraph.

- Working knowledge of agent design patterns: tool and function calling, structured output, planning and reflection loops, multi-agent handoffs, and knowing when a deterministic workflow beats an agent.

- Experience building RAG systems with a vector or hybrid search store, such as Azure AI Search, PostgreSQL with pgvector, or Cosmos DB.

- Prompt engineering depth, including system prompt design, few-shot strategy, context window management, and systematic iteration against an eval set.

- Hands-on experience with Docker and Kubernetes, including writing production images, managing configuration and secrets, and deploying and scaling workloads on AKS or equivalent.

- Experience deploying and operating services on Azure, such as AKS, Container Apps, Azure Functions, or App Service.

- Solid API and data fundamentals: REST,



async programming, SQL, and JSON schema design.

- Git-based workflow and experience shipping through CI/CD.

- Excellent communication skills, with the ability to articulate complex technical concepts to diverse audiences, including non-technical stakeholders, and to set realistic expectations about AI capability and risk.

- Exceptional analytical and debugging skills, including the ability to diagnose why an agent failed when the failure is non-deterministic and the stack trace is clean.

- Strong knowledge and experience in working with customers in a consultative approach in a technical environment.

Additional Qualifications

- Experience with Model Context Protocol (MCP) server or client development.

- Familiarity with LLM observability and evaluation tooling, such as Azure AI Foundry evaluations, LangSmith, or OpenTelemetry-based tracing.

- Familiarity with Azure networking, identity, and security requirements, including Managed Identity, Key Vault, and Private Endpoints.

- Experience with fine-tuning, distillation, or small language model deployment.

- Experience with Microsoft Fabric, Azure Databricks, or Azure Synapse for the data layer behind AI solutions.

- Experience with document intelligence and multimodal inputs, such as Azure AI Document Intelligence, vision, or speech.

- Experience in a regulated workplace (HIPAA, SOC 2, GDPR, DPDP Act) with auditability and data residency requirements.

- Infrastructure as code experience (Terraform, Bicep, or Helm).

- Proven ability to manage multiple client projects and deliver high-quality results on time.

- Experience in Azure DevOps or GitHub for source control and pipelines.

📌 Senior Agentic AI Developer (Hyderabad)
🏢 Coretek Services India
📍 Hyderabad