Principal AI/ML Engineer

1 day ago

Bangalore, India Optum Full time

Lead innovation in deep learning, foundation models, reinforcement learning, or multimodal AI

Key Skills:

  • GenAI, Python coding
  • Research-level knowledge of LLMs, vision transformers (ViTs), diffusion models, RL, or multi-agent systems
  • RAG & Vector Databases: Expertise in building and querying knowledge bases using Retrieval-Augmented Generation with Azure AI Search or similar technologies
  • LLM Fine-Tuning: Hands-on experience with efficient finetuning techniques (LoRA/QLoRA) for specializing models on custom datasets
  • Proficiency with libraries for data transformation and comparison, such as JSON Patch and DeepDiff
  • Quantum Computing: Understanding of quantum algorithms and tools like IBM's Qiskit and Google's Cirq.
  • Familiarity with Multimodal transformers - integrating text, image, and audio data to create models
  • Experience on graph-based RAGs for contextual reasoning and incorporating knowledge connections from graph neural networks. Real time RAG systems to handle dynamic and up-to-date information
  • Track record of research (papers, patents, open source)

Must have skills

  • LLM & RAG Architecture Expertise:
  • Hands-on experience with Retrieval-Augmented Generation (RAG) architectures using embeddings via Azure AI Search and Databricks.
  • Proficient in implementing semantic search capabilities.
  • Familiar with MCP servers for scalable deployment.
  • Agentic AI & Orchestration: (Must have differentiating factor will be level of expertise)
  • Experience with autonomous decision support using LangGraph.
  • Skilled in agent orchestration using Microsoft Copilot Studio and CrewAI.
  • Performance Optimization: (Must have differentiating factor will be level of expertise)
  • Working knowledge of latency reduction techniques for RAG-based applications, including Redis-based caching.
  • LLM Fine-Tuning: (Must have for differentiating factor will be level of expertise)
  • Practical understanding of fine-tuning methods such as LoRA (Low-Rank Adaptation).
  • Model Selection & Prompting: (Must have differentiating factor will be level of expertise)
  • Awareness of the latest LLMs tailored to specific use cases (e.g., Claude, Gemini, GPT series).
  • Understanding of prompt engineering requirements across different models.
  • Cost Estimation: (Must have differentiating factor will be level of expertise)
  • Ability to calculate and optimize costs for API-based model usage.
  • Functional/Team experience
  • Expertise with diverse AI uses cases
  • Business and Domain Understanding
  • Track record of research (papers, patents, open source)

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