Lead Platform Engineer – Agentic System

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About the Role

Agentic AI Platform

Lead AI Platform Engineer

Key Responsibilities

• Architect & Build Agentic Systems:

  Design, develop, and deploy multi-agent workflows leveraging frameworks such as

  CrewAI, LangGraph

  , or custom-built orchestration layers.

• Platform Engineering:

  Construct the underlying

  agentic runtime

  , encompassing message routing, memory management, and context-sharing mechanisms between agents.

• LLM & Tool Integration:

  Integrate Large Language Models (e.g., OpenAI, Anthropic, or open-source alternatives), vector databases, retrieval systems, and external APIs for agent tool-use and reasoning capabilities.

• Workflow Design & Optimization:

  Collaborate with AI researchers and solution engineers to design dynamic agent workflows that adapt based on contextual information and analytical results.

• Cloud & Scalability:

  Architect scalable, cost-efficient deployments utilizing

  AWS, GCP, or Azure

  , leveraging cloud-native components (e.g., Lambda, ECS, Kubernetes, Pub/Sub) to ensure high availability and performance.

• Observability & Governance:

  Implement comprehensive monitoring, evaluation metrics, and safety checks for autonomous agents to ensure reliability and compliance.

• Team Leadership & Mentorship:

  Provide guidance to a team of engineers, establish best practices for agent development, and cultivate a high-performance engineering culture. Take sessions for the team and build tog-grade skills in the area across the company.

Required Qualifications

• Strong background in Python

  , with demonstrated experience in asynchronous programming, API development, and distributed systems.
• Proven experience in building

  LLM-powered multi-agent systems

  using

  CrewAI

  ,

  LangGraph

  ,

  LangChain

  , or similar orchestration frameworks.
• Deep understanding of

  prompt engineering

  ,

  RAG pipelines

  , and

  tool-calling mechanisms

  .
• Hands-on experience with

  cloud infrastructure

  (AWS/GCP/Azure) and

  MLOps

  components (e.g., Kubernetes, Docker, CI/CD, API Gateway).
• Solid understanding of

  state management

  ,

  context persistence

  , and

  memory architecture

  for agents.
• Experience integrating

  vector stores (e.g., FAISS, Pinecone, Chroma, Weaviate)

  and

  LLM APIs

  (e.g., OpenAI, Claude, Gemini).
• Demonstrated ability to

  architect scalable AI systems

  from prototype to production.
• Excellent communication and collaboration skills, with the ability to translate complex technical concepts into actionable platform features.
• Experience with

  Graph-based orchestration

  or

  LangGraph advanced workflows

  .
• Familiarity with

  CrewAI “crew and task” paradigms

  or other agent coordination frameworks.

Nice-to-Have

• Exposure to

  event-driven architectures

  ,

  message queues

  , or

  knowledge graphs

  .
• Understanding of

  AI safety, alignment

  , and

  governance principles

  .
• Contributions to open-source agent frameworks or AI orchestration tools.
• Ability to customize models to specific purposes.

What We Offer

• An unparalleled opportunity to

  build the next-generation Agentic AI Platform

  from inception.
• The chance to work with

  cutting-edge LLM orchestration technologies

  and

  cloud-native AI systems

  .
• Competitive compensation and potential equity options.
• A collaborative, innovation-driven culture with a strong focus on real-world impact.