Senior Agentic AI Developer

Coretek

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

Posted 9/20/2026full-timeKondapur • IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying agentic AI applications on Azure, with a strong focus on LLM-based systems, retrieval-augmented generation, and API integration. Proven ability to mentor junior engineers and manage client engagements while ensuring high-quality deliverables.

Highest-signal resume keywords

Python DevelopmentLLM API ExperienceAzure DeploymentDocker and KubernetesPrompt Engineering

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Hard Skills

Multi-Step ReasoningTool/Function CallingRetrieval-Augmented GenerationVersion ControlAutomated TestingSQLAPI DesignHybrid SearchFine-TuningInfrastructure as Code

Soft Skills

Excellent CommunicationAnalytical SkillsConsultative Technical SkillsProject Management

Tools & Technologies

Azure AI FoundryMicrosoft Agent FrameworkDockerKubernetesAzure DevOpsGitHubTerraformBicepAzure FunctionsAzure Databricks

Industry Keywords

HIPAASOC 2GDPRDPDP ActMCP Server

Tech Stack

Tools & technologies

AzureDockerKubernetesPostgresPythonSQLTerraformVault

About the role

Key responsibilities & impact

Design, build, and ship agentic AI applications on Azure, including multi-step reasoning, tool/function calling, retrieval, memory, and human-in-the-loop checkpoints.
Implement retrieval-augmented generation pipelines with chunking, embeddings, indexing, hybrid/semantic search, reranking, and citation grounding.
Integrate agents with client systems through APIs, databases, and MCP servers; write tool definitions and schemas.
Build evaluation harnesses and golden datasets measuring accuracy, groundedness, and task completion.
Instrument production agents with tracing, token/cost telemetry, latency budgets, failure/fallback paths, and quality-regression alerting.
Implement guardrails for prompt injection defense, content filtering, PII handling, output validation, and human-approval boundaries.
Tune cost and latency through model selection, prompt caching, context management, batching, and model routing.
Apply version control, code review, automated testing, CI/CD, and prompt/model versioning.
Containerize and deploy agent services; own build, release, scaling, and runtime configuration.
Set technical direction on client engagements and mentor junior engineers.
Work directly with clients to scope agent use cases and assess model capabilities and limitations.
Partner with project managers, data engineers, and application teams to deliver end to end and document systems for operation.

Requirements

What you’ll need

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.
Practical experience with LLM APIs and agent frameworks, such as Microsoft Agent Framework, Azure AI Foundry, Azure OpenAI, or LangGraph.
Working knowledge of tool and function calling, structured output, planning and reflection loops, multi-agent handoffs, and deterministic workflows.
Experience building RAG systems with a vector or hybrid search store, such as Azure AI Search, PostgreSQL with pgvector, or Cosmos DB.
Prompt engineering experience, 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 production images, configuration and secrets, and deploying/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 for explaining complex technical concepts to diverse audiences and setting realistic expectations about AI capability and risk.
Exceptional analytical and debugging skills, including diagnosing non-deterministic agent failures.
Strong experience working with customers in a consultative technical environment.
Experience with 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.
Experience with document intelligence and multimodal inputs, such as Azure AI Document Intelligence, vision, or speech.
Experience in regulated environments involving HIPAA, SOC 2, GDPR, or DPDP Act.
Infrastructure as code experience with 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.