Staff AI Scientist

gehealthcare

Bengaluru, India NM Years Exp Posted 65d ago

Job Description

  • Conduct advanced research in artificial intelligence, with focus areas including machine learning, deep learning, generative AI, large language models, natural language processing, GANs, multimodal AI, and agentic AI systems.
  • Design, prototype, and validate novel AI algorithms, architectures, and workflows for real-world use cases.
  • Explore and apply cutting-edge approaches in transformers, fine-tuning, retrieval-augmented generation (RAG), prompt optimization, autonomous agents, multi-agent systems, model alignment, and reasoning frameworks.
  • Lead experimentation across model training, evaluation, benchmarking, and optimization.
  • Stay current with emerging AI advances and translate academic research and industry innovation into scalable enterprise solutions.
  • Publish research findings, contribute to patents, or create internal technical thought leadership that advances the organization’s AI maturity.
  • Build, fine-tune, and optimize ML/DL models, including supervised, unsupervised, reinforcement, and self-supervised learning systems.
  • Develop and deploy LLM-powered applications, conversational AI, summarization systems, semantic search, knowledge assistants, and intelligent automation platforms.
  • Create Generative AI applications using foundation models for text, image, code, synthetic data, and multimodal outputs.
  • Design and implement GAN-based solutions for synthetic data generation, image synthesis, anomaly simulation, data augmentation, and domain-specific generative use cases.
  • Develop Agentic AI systems capable of task planning, tool usage, workflow orchestration, memory integration, retrieval, and decision support.
  • Use AWS Bedrock to build and scale foundation model applications, including model access, orchestration, secure integration, and GenAI experimentation.
  • Use AWS SageMaker for model training, tuning, experimentation, MLOps, deployment, and monitoring at scale.
  • Work with structured and unstructured data across large-scale datasets to support AI research and production systems.
  • Lead or collaborate on data cleaning, feature engineering, data quality improvement, dataset curation, and annotation strategies.
  • Build robust AI pipelines that integrate with enterprise data systems, APIs, cloud services, and downstream applications.
  • Apply SQL, NoSQL, database modeling, and data warehousing concepts to support efficient model training and inference.
  • Partner with engineering teams to productionize models with scalability, observability, reliability, and security in mind.
  • Ensure all AI systems are designed and deployed with strong Responsible AI principles.
  • Develop practices for fairness, transparency, interpretability, explainability, privacy, accountability, and bias mitigation.
  • Assess risks associated with foundation models, LLM outputs, hallucinations, model drift, adversarial misuse, and unsafe automation.
  • Implement guardrails, evaluation standards, governance frameworks, and human-in-the-loop processes where necessary.
  • Support compliance with evolving data privacy, security, and ethical AI requirements.
  • Translate complex AI concepts into clear business value propositions for stakeholders, leadership teams, and non-technical audiences.
  • Collaborate with product, engineering, security, legal, data, and business teams to define AI strategy and deliver measurable outcomes.
  • Mentor junior scientists, ML engineers, and data professionals.
  • Contribute to roadmap planning, architecture reviews, technical hiring, and AI capability development across the organization.

 

Required Qualifications

  • PhD or Masters in Computer Science, Artificial Intelligence, Machine Learning, NLP, Data Science, or a related quantitative discipline.
  • Strong research background with demonstrated contributions in AI/ML through publications, patents, applied research, industrial innovation, or equivalent scientific work.
  • Deep knowledge of Machine LearningDeep LearningNatural Language ProcessingGenerative AILarge Language ModelsAgentic AI / AI Agents
  • Proven experience developing advanced AI models from research through im

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