Generative AI/ML Engineer

16 hours ago

Mumbai, Maharashtra, India dentsu Full time

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

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