It starts with you – a senior AI engineer focused on building production-grade agentic systems: autonomous agents, MCP tools, agentic workflows, and rigorous evaluation.
If you want to grow your skills building AI products for mission-critical AI, join our companys mission – this role is for you.
The Responsibilities
Design and build single- and multi-agent systems with planning, memory, and tool use.
Implement and operate MCP tools and servers with secure schemas and permissions.
Build agentic workflows using LangChain, LangGraph, or equivalent frameworks.
Integrate LLMs via SDKs; manage prompts, structured outputs, and tool calling.
Define and run LLM evals (quality, correctness, latency, cost, regressions).
Add observability and reliability (logging, tracing, retries, state management).
Optimize performance and cost; take systems to production.
Mentor engineers and establish agentic best practices.
If you want to grow your skills building AI products for mission-critical AI, join our companys mission – this role is for you.
The Responsibilities
Design and build single- and multi-agent systems with planning, memory, and tool use.
Implement and operate MCP tools and servers with secure schemas and permissions.
Build agentic workflows using LangChain, LangGraph, or equivalent frameworks.
Integrate LLMs via SDKs; manage prompts, structured outputs, and tool calling.
Define and run LLM evals (quality, correctness, latency, cost, regressions).
Add observability and reliability (logging, tracing, retries, state management).
Optimize performance and cost; take systems to production.
Mentor engineers and establish agentic best practices.
Requirements:
4+ years software engineering; 2+ years production AI/ML systems.
Hands-on experience with agentic architectures and tool calling.
Practical experience with MCP.
Experience with LangChain, LangGraph, or equivalent frameworks.
Proven experience designing and operating LLM evals.
Strong Python and API design skills.
Familiarity with RAG pipelines, vector databases, and embedding-based retrieval systems (indexing, chunking, filtering, relevance tuning).
4+ years software engineering; 2+ years production AI/ML systems.
Hands-on experience with agentic architectures and tool calling.
Practical experience with MCP.
Experience with LangChain, LangGraph, or equivalent frameworks.
Proven experience designing and operating LLM evals.
Strong Python and API design skills.
Familiarity with RAG pipelines, vector databases, and embedding-based retrieval systems (indexing, chunking, filtering, relevance tuning).
This position is open to all candidates.
