Senior Agentic AI Developer

Coretek Services India; Hyderabad; Hybrid; INR 300,000 - 540,000; Full time

Job summary

Coretek Services India is seeking an experienced ML/AI engineer to design and deploy agentic AI applications on Azure. You will implement retrieval-augmented generation, integrate agents with client systems, and build robust evaluation and observability harnesses.

The role emphasizes practical, production-grade AI, with a focus on cost, latency, and safety. The ideal candidate has strong Python skills, extensive experience with LLM APIs and agent frameworks, and a proven track record delivering

Qualifications

• At least 7 years in a software, data, or ML engineering role.
• 2+ years building LLM-based or agentic applications that reached real users.
• Hands-on Python development with testing, packaging, and code review.
• Experience with LLM APIs and agent frameworks (e.g., Microsoft Agent Framework, Azure AI Foundry, Azure OpenAI, LangGraph).
• Knowledge of agent design patterns: tool calling, structured output, planning loops, multi-agent handoffs.
• Experience building RAG systems with vector stores or hybrid search (Azure AI Search, pgvector, Cosmos DB).
• Prompt engineering depth: system prompts, few-shot strategies, context window management.
• Docker and Kubernetes in production: images, config, secrets, AKS deployment.

Responsibilities

• Design, build, and ship agentic AI applications on Azure with multi-step reasoning and tool calls.
• Implement retrieval-augmented generation pipelines with embedding, indexing, and grounding.
• Integrate agents with client systems via APIs, databases, and MCP servers; define tools and schemas.
• Build evaluation harnesses and datasets; quantify accuracy and grounding.
• Instrument production agents with tracing, telemetry, latency budgets, and alerting on quality regressions.
• Implement guardrails: prompt injection defense, content filtering, PII handling, and safe boundaries.
• Tune cost and latency via model selection, caching, context mgmt, batching, routing to smaller models.
• Apply engineering discipline: version control, code review, automated tests, CI/CD, and versioning.

Skills

Python development

LLM APIs

Agent frameworks

RAG systems

Prompt engineering

Git CI/CD

REST / SQL / JSON

Communication

Debugging

Consultative customer work

Tools

Docker

Kubernetes

AKS

Job description

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 environment (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.