Artificial Intelligence Engineer

1 week ago

Ahmedabad, Gujarat, India Space-O Technologies Full time ₹ 15,00,000 - ₹ 25,00,000 per year

Key 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 infrastructure

Required Skills & Qualifications

Must-Have Experience

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

Nice to Have

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

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