AI Solution Architect

AI Solution Architect

Key Responsibilities

API & Microservices Development

• Design and implement robust

  asynchronous APIs

  using

  FastAPI

  for GenAI microservices.
• Ensure request routing, rate limiting, error tracking, and observability for production-grade systems.

Multi-Agent Orchestration

• Architect

  multi-agent systems

  using

  LangGraph, CrewAI, or similar frameworks

  .
• Implement dynamic workflows with

  LangChain Expression Language (LCEL)

  and tool/function calling for complex task orchestration.

RAG & Knowledge Systems

• Build

  retrieval-augmented generation (RAG) pipelines

  with advanced chunking, metadata tagging, and

  vector search integration

  .
• Work with

  vector databases

  such as FAISS, Pinecone, and GCP Matching Engine.

Caching & State Management

• Develop

  session management layers

  and caching mechanisms using

  Redis (pub/sub, aioredis)

  to enable memory and persistence in real-time chat systems.

Cloud Deployment & LLM Optimization

• Deploy and optimize

  LLM applications

  on

  Google Cloud Platform

  (Vertex AI, Cloud Run, Storage, IAM, Matching Engine).
• Integrate embedding models from

  OpenAI, Cohere, and Gemini

  .

Security & Compliance

• Implement

  API key management, JWT-based authentication, and audit logging

  .
• Maintain industry-standard

  security best practices

  across deployments.

Required Skills & Qualifications

• 5+ years

  of backend engineering experience in Python.
• Strong expertise in

  FastAPI

  with async/await, background tasks, dependency injection, and exception handling.
• Hands-on experience with

  LangChain, LangGraph, LCEL, and multi-agent systems

  .
• Proficiency in

  Redis

  (pub/sub, async clients, caching layers) for conversation state and memory.
• Strong knowledge of

  Google Cloud Platform

  (Vertex AI, Cloud Run, IAM, Storage, Matching Engine).
• Familiarity with

  vector databases

  (FAISS, Pinecone, GCP Matching Engine) and

  embedding models

  (OpenAI, Cohere, Gemini).
• Experience with

  tool/function calling, session tracking, and context management

  in LLMs.
• Proficiency with

  Docker

  and building scalable microservice architectures.

Preferred Skills (Nice to Have)

• Exposure to

  observability tools

  (Prometheus, Grafana, OpenTelemetry).
• Familiarity with

  CI/CD pipelines

  and automated deployments.
• Experience in

  fine-tuning or custom training of LLMs

  .
• Knowledge of

  MLOps practices

  for AI/ML model lifecycle management.