Home/Jobs/Senior Staff AI Scientist

Senior Staff AI Scientist

GE HealthCare

Bengaluru

5-8 years

1 day ago

$43.4K–65.1K/yr

Full-time

Hybrid

Skills Required LLM

RAG

Agentic AI

Gen AI

Transformers

Prompt Engineering

Vector Database

Multimodal AI

Machine Learning

Deep Learning

NLP

Python

AWS Bedrock

AWS SageMaker

Responsible AI

Description GE HealthCare is hiring a Senior Staff AI Scientist to lead advanced AI research and build production-grade intelligent systems. The role blends scientific depth, engineering strength, and business impact across fast-evolving AI problem spaces.

Company: GE HealthCare

Role: Senior Staff AI Scientist

Location: Bengaluru, Karnātaka, India, 560066 | Hybrid

Experience

- Strong research background in AI/ML with demonstrated contributions through publications, patents, applied research, industrial innovation, or equivalent scientific work
- Proven experience developing advanced AI models from research through implementation and evaluation
- Strong experience with AWS Bedrock and AWS SageMaker for foundation model development, lifecycle management, and deployment workflows
- Robust understanding of Responsible AI, including governance, fairness, explainability, privacy, bias mitigation, and risk control
- Expert-level proficiency in Python
- Strong ability to solve complex, ambiguous, open-ended AI problems
- Excellent verbal and written communication skills
- Strong collaboration skills across research, engineering, product, and leadership teams
- Strong curiosity and commitment to ongoing learning in a rapidly evolving AI landscape

Qualification

- PhD in Computer Science, Artificial Intelligence, Machine Learning, NLP, Data Science, or a related quantitative discipline
- Master's in Computer Science, Artificial Intelligence, Machine Learning, NLP, Data Science, or a related quantitative discipline

Responsibilities

- Conduct advanced research in artificial intelligence across machine learning, deep learning, generative AI, LLMs, NLP, GANs, multimodal AI, and agentic AI systems
- Design, prototype, and validate novel AI algorithms, architectures, and workflows for real-world use cases
- Explore transformers, fine-tuning, retrieval-augmented generation, 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
- Build, fine-tune, and optimize ML/DL models across supervised, unsupervised, reinforcement, and self-supervised learning




- Develop and deploy LLM-powered applications such as 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 for task planning, tool usage, workflow orchestration, memory integration, retrieval, and decision support
- Use AWS Bedrock to build and scale foundation model applications
- 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

Additional Responsibilities





- Work in highly ambiguous and fast-evolving AI problem spaces
- Translate domain challenges into AI opportunities and practical solutions
- Apply mathematical reasoning to model design, tuning, experimentation, and performance analysis
- Craft, test, and optimize prompts for foundation models and LLM-driven applications
- Design prompt strategies that improve relevance, reliability, task completion, and output quality
- Evaluate new tools, methods, and research directions to determine where they create business value

Nice To Have

- Postdoctoral research
- Industrial research lab experience
- Significant applied research leadership in AI
- Strong publication record in reputable AI/ML/NLP conferences or journals
- Experience with multimodal AI, including text, image, audio, video, or document intelligence systems
- Experience with RAG pipelines, vector databases, tool-using agents, and advanced LLM evaluation frameworks
- Familiarity with MLOps, CI/CD for ML, model monitoring, A/B testing, and production observability
- Knowledge of privacy-preserving AI techniques, model security, red teaming, and governance workflows
- Experience leading AI innovation programs or enterprise AI transformation initiatives

More Skills GANs, Transformer architectures, PyTorch, TensorFlow, Keras, model selection, hyperparameter tuning, training optimization, evaluation metrics, model compression, inference performance improvement, text classification, NER, embeddings, summarization, semantic retrieval, question answering, sentiment analysis, conversational AI, fine-tuning, evaluation, grounding, safety controls, foundation models, multimodal generation, agent architecture, orchestration, tool use, function calling, retrieval systems, memory design, reliability engineering, guardrails, multi-step planning, SQL, database querying, database modeling, NoSQL, data warehousing, large-scale data handling, cloud-native AI system design, MLOps, CI/CD for ML, model monitoring, A/B testing, production observability, privacy-preserving AI, model security, red teaming

Prepare for this role

Recommended resources to build the skills for this position. Sponsored.

Top 10 RAG Quick-Prep Questions

Zenaique A shorter RAG interview prep set for quick review.

Top 20 LLM System Design Questions (Quick)

Zenaique A focused LLM system design interview question set.

Top 50 LLM System Design Interview Questions

Zenaique

Curated LLM system design questions from Zenaique.

More LLM jobs

Senior AI-Native Engineer

Payoneer

Gurugram

Today

AI Customer Engineer

Cognizant

Bengaluru

Today

SAP BTP AI Consultant

Infosys Limited

Bengaluru

Today

Senior AI/ML Engineer

Ciklum

Chennai

Today

Senior AI/ML Engineer

Tirget Analystics

Hyderabad

Today

Senior AI Engineer

WSP

Noida

Today

📌 Senior Staff AI Scientist (Bengaluru)
🏢 Top Gen AI Jobs
📍 Bengaluru