AI Engineer
Summary
Build and deploy enterprise AI agents and automation workflows that integrate with internal systems, APIs, and data platforms using Python, LangChain/LangGraph, and cloud infrastructure.
You will design, build, deploy, evaluate, and operate enterprise AI agents and automation workflows. You will integrate agents with internal systems, data platforms, APIs, and services; implement RAG, evaluations, monitoring, guardrails, and secure release processes; and translate business workflows into reliable agent-driven solutions.
Responsibilities
- Design and implement AI agents for multi-step workflows
- Build single-agent and multi-agent architectures
- Develop reusable agent patterns and templates
- Deploy agents on managed AI platforms
- Configure secure execution environments and lifecycle management
- Design human-in-the-loop escalations and approval gates
- Manage prompt and configuration versioning, staged rollouts, and canary releases
- Integrate agents with internal data platforms, APIs, and services
- Implement RAG pipelines, vector databases, and knowledge layers
- Build evaluation frameworks
- Monitor agent behavior and production performance
- Implement error handling, cost controls, and usage guardrails
- Maintain audit trails and explainability
- Implement safeguards against hallucinations, unsafe actions, and data leakage
- Translate business workflows into agent-driven automation
Requirements
- Strong Python programming skills
- Experience with LLM-based applications and agent systems
- Familiarity with LangChain, LangGraph, AutoGen, or CrewAI
- Experience with API development, microservices, and tool orchestration
- Knowledge of vector databases, RAG pipelines, and prompt engineering
- Experience with AWS, Azure, or GCP
- Experience with Docker and Kubernetes
- Experience with CI/CD, production deployment, and observability tooling
- Experience designing goal-driven AI systems and multi-step reasoning workflows
- Ability to implement evaluation pipelines and production guardrails
- Awareness of cost, latency, and performance trade-offs in LLM systems
Benefits
- Daily catered lunch
- Coffee, stocked kitchen, and snack bar
- In-house bar and lounge
- Company boat
- In-house gym
- Nutritionist or personal trainer sessions
- Bi-weekly massages
- Annual company trip
- Global rotations