AI Developer
Indexed description
Job Summary: We are looking for an experienced AI/GenAI Engineer to design, develop, and deploy scalable AI-powered applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI frameworks/workflow development. The ideal candidate should have strong Python development skills, hands-on experience with modern AI orchestration frameworks, and exposure to cloud-native AI deployments.
Experience: 6-10Years
Location: Mumbai
Mode of Work: Hybrid
Key Responsibilities:
- Build and deploy GenAI applications using LLMs such as GPT, Gemini, Llama, Mistral, Claude, etc.
- Design and implement RAG pipelines using vector databases and embeddings.
- Develop AI agents and workflow orchestration using LangChain, LangGraph , CrewAI , AutoGen , or similar frameworks.
- Fine-tune, evaluate, and optimize LLM models for production use cases.
- Integrate AI services into scalable backend systems and APIs.
- Work with structured/unstructured datasets for NLP and AI workflows.
- Deploy AI solutions on cloud platforms such as Azure, AWS, or GCP.
- Build observability, monitoring, guardrails, and evaluation frameworks for GenAI systems.
- Collaborate with product, engineering, and business teams to deliver AI-driven solutions.
- Mentor junior engineers and contribute to AI architecture decisions (for senior roles).
- Experience building production-grade GenAI systems
- Exposure to multi-agent workflows
- Experience with AI observability tools
- Understanding of scalable backend architecture
- Prior work on enterprise AI products or copilots
- Experience withmulti-agent AI systemsand complex workflow orchestration
- Familiarity withfine-tuning techniques( LoRA , PEFT, RLHF) for LLMs
- Exposure toAI observability tools(e.g., LangSmith , Weights & Biases, Arize , PromptLayer )
- Knowledge ofLLM evaluation frameworks, benchmarking, and prompt testing
- Experience implementingAI guardrails, safety, and hallucination reduction strategies
- Understanding ofdata pipelines and ETL processesfor AI/ML workloads
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