AI Engineer

10 hours ago

Ahmedabad, Gujarat, India Space-O Technologies Full time ₹ 20,00,000 - ₹ 25,00,000 per year

Experience: 4+ Years

Job Type: Full-time

Location: Ahmedabad

Posted: September 7, 2025

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), (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

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