This is a remote position.

Senior AI & Agentic System Engineer

About the Role

We are looking for a Senior AI & Agentic System Engineer to design and build production-grade AI agents that can reason, use tools, interact with APIs, and execute complex multi-step workflows.

You’ll work at the intersection of LLM engineering, agent architecture, backend systems, and cloud infrastructure, with significant ownership over architecture, reliability, evaluation, and scalability.

Requirements

Key Responsibilities

• Design and build autonomous AI agents capable of tool use, web/API interaction, memory, and multi-step task execution.
  
• Architect agentic workflows including planner/executor/verifier, multi-agent, HITL, retry, and failure-recovery patterns.
  
• Integrate LLM providers such as OpenAI, Anthropic, and Gemini, including structured outputs, function calling, context management, and prompt optimization.
  
• Build RAG and knowledge pipelines using embeddings, vector databases, hybrid search, reranking, and agent memory.
  
• Develop scalable backend services using Python and/or Node.js/TypeScript, REST/GraphQL APIs, async processing, and background jobs.
  
• Design reliable distributed systems with appropriate patterns for state persistence, caching, idempotency, event-driven workflows, and fault tolerance.
  
• Build observability and evaluation frameworks for AI systems, including tracing, behavioral regression testing, latency, quality, and token-cost monitoring.
  
• Own CI/CD and cloud deployments across development, staging, and production environments.
  
• Evaluate emerging technologies such as MCP, A2A, reasoning models, and agent frameworks and recommend practical adoption.
  

Required Skills

• 5+ years of software/backend engineering experience with strong system-design expertise.
  
• Hands-on experience building production AI agents, beyond basic chatbot implementations.
  
• Strong experience with at least one major LLM provider SDK: OpenAI, Anthropic, or Gemini.
  
• Strong understanding of prompt engineering, tool/function calling, structured outputs, context management, and token optimization.
  
• Experience with agent frameworks/patterns such as LangGraph, CrewAI, AutoGen, ReAct, or plan-and-execute.
  
• Strong Python and/or Node.js/TypeScript development skills.
  
• Experience with RAG, embeddings, vector databases, hybrid retrieval, and reranking.
  
• Strong knowledge of REST/GraphQL APIs, asynchronous processing, databases, and distributed systems.
  
• Hands-on AWS experience, particularly Lambda, ECS/Fargate, API Gateway, S3, SQS, SNS/EventBridge, IAM, and CloudWatch.
  
• Experience with Docker, GitHub Actions, and Terraform.
  
• Understanding of AI security, including prompt injection, guardrails, output validation, and safe tool execution.
  

AI/LLMOps & Evaluation

Experience with tools such as:

• Langfuse, LangSmith, Helicone, or Arize Phoenix
  
• RAGAS, DeepEval, Promptfoo, or Braintrust
  
• LLM tracing, behavioral evaluations, regression testing, and cost monitoring.
  

Nice to Have

• Browser automation using Playwright or Puppeteer.
  
• Experience with multimodal/vision-based agents.
  
• Knowledge of GraphRAG and knowledge graphs.
  
• Experience with Temporal or durable workflow execution.
  
• Open-source contributions to AI tooling.
  
• Background in ML/data science alongside software engineering.
  

Tech Stack