Generative AI/ML Engineer

Role Summary

Generative AI/ML Engineer

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