AI Engineer

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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