0.0 years

0 Lacs

mumbai, maharashtra, india

On-site

Job Description Role Summary We are seeking a Generative AI/ML Engineer to design, build, and deploy intelligent AI solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic workflows, and edge computing. You will work hands-on with cloud-native AI services, LLMOps pipelines, and enterprise-grade deployment patterns to solve business problems. Key Responsibilities Design, develop, and fine-tune LLM-powered applications for enterprise use cases. Experience in evaluating LLM applications and developing observability frameworks Implement RAG pipelines using vector databases, embeddings, and optimized retrieval strategies. Build agentic AI workflows with multi-step reasoning, tool calling, and integration with APIs. Integrate GenAI solutions into multi-cloud or hybrid cloud environments (AWS, Azure, GCP). Develop and optimize edge AI deployments for low-latency use cases. Create data strategy, ingestion, transformation, enrichment, validations, quality checks via pipelines for AI ingestion, preprocessing, and governance. Implement AI safety, bias mitigation, and compliance measures. Work closely with LLMOps teams to enable continuous integration & deployment of AI models. Write well-documented, production-ready code in Python, Node.js, Rust . Benchmark and evaluate model performance , latency, and cost-efficiency. Required Skills Proficient in cloud AI services (e.g. AWS Bedrock/SageMaker, Azure AI Foundry, Google Vertex AI, Anthropic, OpenAI APIs). Strong proficiency with Python and LLM frameworks (e.g. PromptFlow, LangGraph, LlamaIndex, HuggingFace, PyTorch, TensorFlow). Hands-on experience with vector DBs (e.g. Pinecone, Weaviate, Milvus, FAISS, Azure Cognitive Search). Experience building RAG-based and agentic AI solutions. Familiarity with Edge AI frameworks (e.g. NVIDIA Jetson, AWS IoT Greengrass, Azure Percept). Multi-modal AI (text, image, speech, video) experience. Strong grasp of APIs, microservices, and event-driven architectures . Knowledge of AI governance (data privacy, model explainability, security). Experience in containerized deployments (Docker, Kubernetes, serverless AI functions). Preferred Skills Generative agents with memory and planning capabilities . Real-time AI streaming with WebSockets or Kafka. Prior contributions to open-source GenAI projects . Experience in build, test and deploy various ML models Experience in building MCP, A2A protocol Show more Show less

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