Overview

We are looking for an exceptional Sr Staff AI Scientist with a strong research background and deep expertise in Machine Learning, Deep Learning, GANs, NLP, Generative AI, LLMs, and Agentic AI. This role is ideal for a highly analytical and innovation-driven professional who can lead advanced AI research, design production-grade intelligent systems, and translate emerging AI capabilities into real business impact.

Responsibilities

• 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.

Qualifications

• Strong research background with demonstrated contributions in AI/ML through publications, patents, applied research, industrial innovation, or equivalent scientific work.
• Deep knowledge of Machine Learning, Deep Learning, Natural Language Processing, Generative AI, Large Language Models, Agentic AI / AI Agents
• Proven experience developing advanced AI models from research through implementation and evaluation.
• Strong experience with AWS Bedrock and AWS SageMaker for foundation model development, model lifecycle management, and deployment workflows.
• Strong understanding of Responsible AI, including model governance, fairness, explainability, privacy, bias mitigation, and risk control.
• Expert-level proficiency in Python as the top priority language for AI and ML development.
• Ability to build efficient, scalable, and production-ready code for research and enterprise AI applications.
• Strong understanding of core ML concepts, including Transformer architectures
• Hands-on experience with leading frameworks such as PyTorch, TensorFlow, Keras
• Strong expertise in NLP techniques, including text classification, NER, embeddings, summarization, semantic retrieval, question answering, sentiment analysis, and conversational AI.
• Expertise in Generative AI architectures, foundation models, and enterprise use cases involving text, image, document, and multimodal generation.
• Strong experience building AI agents and autonomous workflows.
• Skills in, Agent architecture and orchestration, Tool use and function calling, Retrieval systems, Memory design, Reliability engineering, Evaluation and guardrails, Multi-step planning and execution
• Familiarity with modern agent frameworks and orchestration patterns for enterprise-grade agentic systems.
• Ability to work with diverse datasets and establish strong data foundations for AI systems.
• Ability to apply mathematical reasoning to model design, tuning, experimentation, and performance analysis.
• Strong experience with AWS Bedrock, AWS SageMaker, AWS data and ML services relevant to AI model development and deployment
• Familiarity with cloud-native AI system design, scalable training, model serving, monitoring, and MLOps practices.
• Strong commitment to designing fair, accountable, transparent, and human-centered AI systems.
• Ability to identify, assess, and mitigate ethical risks in model design, training data, inference, and deployment.
• Expertise in crafting, testing, and optimizing prompts for foundation models and LLM-driven applications.
• Ability to design prompt strategies that improve relevance, reliability, task completion, and output quality.
• Skill in translating domain challenges into AI opportunities and practical solutions.
• Strong ability to solve complex, ambiguous, and open-ended AI problems.
• Comfortable navigating evolving requirements, incomplete data, experimental uncertainty, and rapid technological change.
• Excellent verbal and written communication skills.
• Ability to explain technical concepts, model limitations, trade-offs, and business implications to both technical and non-technical stakeholders.
• Strong collaboration skills across research, engineering, product, and leadership teams.
• Strong curiosity and commitment to ongoing learning in a rapidly evolving AI landscape.
• Ability to evaluate new tools, methods, and research directions and determine where they create business value.

Nice to have

• Postdoctoral research, industrial research lab experience, or significant applied research leadership in AI.
• Strong publication record in reputable AI/ML/NLP conferences or journals.

Education

• PhD or master's in computer science, Artificial Intelligence, Machine Learning, NLP, Data Science, or a related quantitative discipline.