AI Engineer

Job Description

We’re looking for a Senior AI Developer with deep expertise in building AI agent systems, retrieval-augmented generation (RAG) pipelines, and deploying production-grade GenAI applications. You will be a core contributor in designing, developing, and scaling intelligent agentic workflows using modern frameworks like LangGraph, CrewAI, and LangChain. This is a high-impact role for someone passionate about LLMs, context-aware automation, and multi-agent orchestration.

Required Qualifications

• Programming – Python (advanced), Typescript/Node.js (nice to have)
  

• AI Frameworks -LangGraph, CrewAI, LangChain, LlamaIndex, OpenAI, Hugging Face
  
• Agent Systems – Designing task-oriented agents with memory, tool use, planning, and inter-agent communication
  
• RAG Architecture – Document loaders, chunking strategies, vector embedding models, hybrid search (BM25 + vector), contextual reranking
  
• LLM Tooling – OpenAI GPT-4/4o, Claude, Gemini, local models (e.g., Mistral, LLaMA)
  
• Infrastructure – Vector DBs (e.g., Weaviate, Pinecone, Qdrant, Elasticsearch), Postgres, MongoDB
  
• MLOps – Prompt engineering, model evaluation, A/B testing, telemetry, observability
  
• Deployment – REST APIs, FastAPI, Docker, CI/CD pipelines
  
• Other – Strong written and verbal communication; ability to work independently and own initiatives end to end
  
• Experience deploying multi-agent systems in real-world products.
  
• Familiarity with graph-based orchestrators like LangGraph and node-level planning.
  
• Contributions to open-source AI tools or published work in LLM/agentic systems.
  
• Exposure to GenAI safety, audit logging, and access controls in enterprise settings

Roles and Responsibilities

• Design and implement AI agent frameworks for task decomposition, tool use, memory handling, and multi-turn conversations.
  

• Build and optimize RAG pipelines using tools like LangChain, LlamaIndex, or custom vector search setups.
  
• Integrate agents with internal tools, APIs, and databases to support real-world use cases (e.g., customer support, scheduling, workflow automation).
  
• Collaborate with ML researchers and product teams to experiment with novel architectures and orchestrators like LangGraph and CrewAI.
  
• Monitor and evaluate model performance across various use cases using telemetry and custom analytics.
  
• Ship production-ready systems with robust logging, testing, and monitoring pipelines.
  
• Stay up-to-date with the latest in LLMs, open-source agentic frameworks, and vector search infrastructur