AI Engineer / Agentic AI Engineer
Core
Design and implement advanced AI solutions (ML, GenAI, Agentic AI) for enterprise clients, acting as a bridge between business needs and scalable technology architectures.
Role type
Senior IC Agentic AI Engineer / Consultant
Builds
Scalable GenAI and multi-agent systems, RAG architectures, and production-ready AI workflows for large enterprise clients.
Domain
Professional Services / Enterprise AI & Generative AI
Deliverable
production ML models | product features | client delivery
Required skills
Python, Machine Learning, Deep Learning, Generative AI & LLMs, Prompt Engineering, RAG systems, Agentic AI & Multi-agent architectures, Vector databases, Knowledge Graphs, LLMOps, Data Engineering, SQL, Cloud platforms (Azure/AWS/GCP)
Preferred skills
Agent orchestration frameworks (LangGraph, AutoGen, CrewAI), Workflow engines (Temporal, Prefect), AI governance & security, Responsible AI practices
Technologies
Python, TensorFlow, PyTorch, JAX, ONNX, LangChain, LlamaIndex, Haystack, DSPy, LangGraph, AutoGen, CrewAI, Semantic Kernel, PydanticAI, Neo4j, Amazon Neptune, Databricks, Snowflake, Azure, AWS, GCP
Responsibilities
Pilot the design, development, and validation of ML models aligned with business objectives; Design and deliver end-to-end GenAI solutions including RAG and enterprise LLM applications; Implement agentic AI systems with multi-agent architectures and tool usage; Make architectural decisions between ML, GenAI, and agentive approaches; Evaluate and select foundational models and define hybrid GenAI architectures; Develop vector databases, knowledge graphs, and retrieval pipelines; Define and implement LLMOps and AgentOps frameworks; Establish security guardrails and human-in-the-loop processes; Lead PoC, MVP, and industrialization of production systems; Conduct client workshops and advise on AI strategy.
Seniority
Senior, hands-on IC with client-facing autonomy
