
AI Engineer
4 weeks ago
Experience: 4+ Years
Job Type: Full-time
Location: Ahmedabad
Posted: September 7, 2025
Job DescriptionWe'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), (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
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
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