We are looking for an experienced AI Engineer specializing in Agentic AI, LLM workflows, and Harness Engineering to design, develop, deploy, and scale AI-powered solutions in production environments.

The ideal candidate will have hands-on experience delivering production-grade AI applications, particularly involving LLM agents, Retrieval-Augmented Generation (RAG), reusable agent skills, and workflow orchestration. The role requires strong engineering ownership, practical experience with Claude Code/Cowork, and the ability to work directly with enterprise customers and non-technical stakeholders.

We prioritize the depth of hands-on AI engineering and successful production delivery over years of experience.

Key Responsibilities

- Design, develop, and deploy production-ready AI-powered applications, agent workflows, and LLM-based solutions.

- Build and maintain agent harnesses, including control loops, tool integrations, skill surfaces, context and state management, and human-in-the-loop checkpoints.

- Orchestrate multi-step agent workflows using AWS Step Functions, Amazon Bedrock AgentCore, AWS Lambda, and related AWS services.

- Develop reusable agent skills, integrate external tools and APIs, and implement MCP-based integrations.

- Design and optimize RAG pipelines, including document chunking, embeddings, vector search, and retrieval strategies.

- Implement deterministic workflow controls, policy-based routing, retries, exception handling, timeouts, and loop guards.

- Establish observability, evaluation, testing, and monitoring practices to ensure the reliability, quality, and scalability of AI workflows.

- Implement AI safety measures, guardrails, prompt-injection defenses, and human-approval mechanisms.

- Work directly with enterprise customers and business stakeholders to gather requirements, present solutions, and translate business needs into technical implementations.

- Own end-to-end solution delivery with minimal supervision, from solution design and development through deployment, monitoring, and continuous improvement.

Essential Requirements

- Proven software engineering experience with hands-on AI engineering solutions successfully deployed to production.

- Experience delivering at least one production project involving LLM agents,



reusable agent skills, or RAG pipelines.

- Practical experience designing, deploying, and scaling AI-powered production workflows using Claude Code and/or Cowork.

- Strong understanding of LLM APIs, tool use, function calling, streaming, prompt engineering, and context management.

- Experience with agent orchestration, workflow design, tool integration, and managing LLM-driven application state.

- Ability to independently own technical solutions and deliver outcomes with minimal guidance.

- Experience engaging directly with non-technical stakeholders, gathering requirements, and presenting technical solutions in a clear and structured manner.

- Strong problem-solving skills and the ability to translate business requirements into reliable, scalable AI solutions.

Added Advantages / Positive to Have

1. Harness Engineering & AWS Agent Orchestration

- Excellent understanding of agent harness architecture, including control loops, tools, skills, context management, state handling, and human-in-the-loop checkpoints.

- Hands-on experience with AWS Step Functions and Amazon States Language (ASL) to orchestrate agent steps, branching, retries, parallel execution, and human-approval gates.

- Experience implementing task-token-based approval workflows and invoking Amazon Bedrock AgentCore runtimes and AWS Lambda from state machines.

- Understanding of deterministic versus LLM-driven control, including policy-based routing, exception handling, timeout management, and loop prevention.

- Practical experience hosting, operating, and scaling agent workloads on Amazon Bedrock AgentCore Runtime and exposing tools through AgentCore Gateway.

- Experience with agent lifecycle management, runtime invocation patterns, and production scaling.

2. Agent Observability, Evaluation & Guardrails

- Hands-on experience with AgentCore Observability, Amazon CloudWatch, Datadog,



or similar monitoring platforms.

- Ability to implement tracing, metrics, logging, and per-step audit trails for debugging and monitoring production agent workflows.

- Experience with AgentCore Evaluations, golden datasets, regression testing, and measuring agent quality before and after releases.

- Practical knowledge of Amazon Bedrock Guardrails and policy controls for prompt-injection defense, personally identifiable information (PII) protection, and hallucination mitigation.

- Experience implementing human-in-the-loop review and approval mechanisms for sensitive or high-risk workflows.

3. Agentic AI with Claude

- Experience designing, deploying, and scaling AI-powered production workflows using Claude Code and/or Cowork.

- Ability to design and publish reusable skills for consumption by agent workflows.

- Strong understanding of prompt engineering, system prompts, iterative refinement, and evaluation-driven improvements.

- Experience building and integrating MCP (Model Context Protocol) servers, including FastMCP, and hosting them on AgentCore or similar platforms.

- Ability to expose tools and enterprise data through MCP servers and APIs, ensuring agents access data through controlled interfaces rather than directly accessing underlying data sources.

- Experience with multi-agent coordination, task delegation, and result aggregation using agent-to-agent (A2A) patterns.

4. RAG & LLM Application Engineering

- Hands-on experience building and optimizing Retrieval-Augmented Generation (RAG) pipelines.

- Understanding of vector databases, document chunking, embeddings, semantic retrieval, and retrieval optimization.

- Experience integrating LLMs with enterprise systems, APIs, and external tools.

- Ability to evaluate and improve retrieval relevance, response quality, reliability, and overall agent performance.

Preferred Background

- Consulting, professional services, or client-facing engineering experience.

- Experience working directly with enterprise customers on AI solution design, deployment, and scaling.

- Demonstrated ownership of production AI solutions from initial requirements through deployment and ongoing optimization.

📌 Senior AI Engineer (Pune)
🏢 Ciklum
📍 Pune