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Wissen Logo Wissen · IT - Software Development

AI Developer

India, Mumbai · Job Posted July 03, 2026
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Job Description

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.

Job Responsibility

  • 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)

Requirements

  • 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

Nice to have

  • Experience with multi-agent AI systems and complex workflow orchestration
  • Familiarity with fine-tuning techniques (LoRA, PEFT, RLHF) for LLMs
  • Exposure to AI observability tools (e.g., LangSmith, Weights & Biases, Arize, PromptLayer)
  • Knowledge of LLM evaluation frameworks, benchmarking, and prompt testing
  • Experience implementing AI guardrails, safety, and hallucination reduction strategies
  • Understanding of data pipelines and ETL processes for AI/ML workloads
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