About The Role
We are looking for an experienced AI Engineer who specializes in building agents and agentic systems—from task-orchestration agents to workflow automation agents, retrieval-augmented agents, research/coding agents, multimodal agents, and domain-specific autonomous agents.
This is a full-stack AI engineering role, ideal for someone who loves shipping: rapid MVPs → stable production, high ownership, and fast problem-solving. Candidates must have built and deployed at least two AI agents in production in the past 12 months and be comfortable operating in high-velocity environments.
What You’ll Do
Build & Deploy AI Agents
- Design, build, and ship agentic workflows across multiple domains (research agents, coding assistants, conversational agents (voice, texts, etc), reasoning agents, scheduling agents, analytics agents, workflow automation bots, etc.).
- Own the end-to-end lifecycle: data ingestion → reasoning → action taking → evaluation → monitoring.
- Build multi-step agents capable of autonomous planning, context tracking, memory, tool use, and API orchestration.
Agent Architecture & Infrastructure
- Architect systems using modern agent stacks (LangChain, LlamaIndex, OpenAI Assistants, Model Context Protocol (MCP), custom orchestration).
- Build robust retrieval pipelines (RAG), vector embeddings, caching layers, and knowledge-grounding systems.
- Integrate agents with external tools and systems (APIs, SaaS apps, CRMs, internal services, databases, messaging platforms).
Productionization
- Deploy agents as microservices with proper observability, evals, guardrails, fallbacks, and monitoring.
- Optimize inference cost, latency, accuracy, and task-completion rates.
- Run systematic evaluations: function calling accuracy, groundedness, hallucinations, long-context stability.
Collaboration & Product Work
- Work closely with product managers, domain experts, and engineers to translate business workflows into agent behaviors.
- Create reusable frameworks and libraries to accelerate subsequent agent builds.
- Document and evangelize agent best practices internally.
Required
What You Bring
- 4–7 years of hands-on experience in AI/ML engineering.
- Successful deployment of at least two production AI agents in the past 12 months (not prototypes).
Expertise In
LLMs: OpenAI, Anthropic, Gemini, Llama, DeepSeek
- Agent frameworks: LangChain, OpenAI Assistants, custom orchestration, state machines
- Retrieval (RAG), vector DBs (Pinecone, Weaviate, Chroma, PGVector)
- API integration & tool-use architectures
- Python/Node for server-side agent logic
- Microservice deployments (Docker, Kubernetes, CI/CD)
- Strong debugging skills across distributed systems, prompt engineering, inference optimization, and agent reasoning traces.
- Comfortable building MVPs in days and scaling them to stable production within weeks/months.
Nice to Have
- Experience building MCP servers or integrating with MCP tools.
- Experience with structured function-calling workflows (JSON schema, tool plans, agent graphs).
- Background in building internal agent frameworks or automation engines.
- Experience designing evaluation frameworks for agents (task completion metrics, scenario tests).
- Familiarity with workflow engines (Temporal, Airflow, Prefect).
Success Looks Like
In Your First 3–6 Months, You Will
- Build and deploy multiple agents that solve real business workflows.
- Improve accuracy, response quality, and reliability of existing agents.
- Establish a reusable internal agent framework to increase build velocity.
- Contribute significantly to cost, latency, and performance improvements.
- Become a core owner of agentic architecture and experimentation.
Why Join Us
- Work directly with founders and senior leaders driving AI-first transformation.
- Build real agents used daily — not research prototypes.
- High autonomy + high impact environment.
- Opportunity to shape the foundation of agentic systems across the org.
- Competitive compensation + massive growth opportunity.