Senior AI Engineer

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 / Good 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.