AI Engineer

Compensation: $60k – $70k • No equity

About the Role

At CodePraxis, we’re building products where AI agents do real work. As an AI Engineer, you’ll own the design and delivery of our AI agents: systems that plan, reason, call tools, and complete multi-step tasks on their own. You’ll work directly with the founders and engineering team, with real ownership of what ships.

Key Responsibilities

Design, build, and deploy production AI agents that plan, use tools, and complete multi-step workflows Build multi-agent systems with clear orchestration, hand-offs, and shared state Develop tool and function-calling integrations that connect agents to our APIs, databases, and third-party services Implement memory and context management (short-term, long-term, RAG) so agents stay accurate across long tasks Build RAG pipelines using embeddings, vector databases, and semantic search Create evaluation frameworks for agents: task success rates, regression tests, trajectory analysis, and guardrails Optimize prompts, latency, and cost across LLMs such as Claude, GPT, and Llama Add observability, tracing, and error handling so agent behavior is debuggable and reliable in production Build safety controls: output validation, permission boundaries, and human-in-the-loop checkpoints Collaborate with product and engineering to turn real user problems into agent-driven features

Requirements

2-3 years of experience in software engineering or AI/ML, with hands-on experience building LLM-based applications Strong Python skills and solid API/backend fundamentals (REST, async, queues) Practical experience building AI agents: tool use, planning, multi-step workflows, and orchestration Experience with frameworks such as LangChain, LangGraph, LlamaIndex, or the Claude Agent SDK Experience with RAG, vector databases, and embeddings Familiarity with cloud platforms (AWS, Azure) and deploying AI systems to production A rigorous approach to evaluating and debugging non-deterministic systems Strong problem-solving skills and the ability to work independently

Nice to Have

Experience with MCP, agent observability tools (LangSmith, Langfuse), or fine-tuning

How to Apply

If you’ve built AI agents that actually work in production, apply with your resume.

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