Overview

AI/ML Engineers code and develop software that deploys ML models and algorithms in production.

Responsibilities

• Research & Development
• Lead applied research in LLMs, generative AI, and multimodal models
• Evaluate and experiment with state-of-the-art architectures (e.g., Transformers, Diffusion Models, Retrieval-Augmented Generation)
• Publish internal whitepapers and contribute to external conferences where applicable
• Engineering & Implementation
• Design and implement scalable AIML pipelines using frameworks like PyTorch, TensorFlow, Hugging Face, and MLflow
• Collaborate with engineering teams to deploy models into production using MLOps best practices (CI/CD, model versioning, monitoring)
• AI/ML Solution Development
• Implement and iterate on AI and machine learning solutions (including Generative AI) by building prototypes, integrating models into services, and improving quality based on evaluation results
• Model Training, Evaluation & Optimization
• Support proof of concepts and model experiments and help baseline performance using clear evaluation metrics (with guidance on approach and tooling)
• Generative AI & Advanced Techniques
• Learn and apply GenAI building blocks such as RAG and prompt orchestration tools (e.g., LangChain); exposure to vector databases is a plus
• Design, develop, and deploy AI-powered solutions to address complex business challenges with emphasis on responsible use of AI
• Support performance and cost improvements (e.g., caching, batching, basic optimization), and learn model optimization techniques (quantization/distillation) as needed
• Software Engineering & APIs
• Contribute to building and testing APIs (e.g., REST with FastAPI) and learn delivery fundamentals such as containers (Docker) and simple app frameworks (e.g., Flask/Streamlit) as needed
• Collaboration & Communication
• Collaborate with cross functional partners to understand business needs, ask clarifying questions, and communicate progress and results clearly
• Leadership, Mentorship & Culture
• Participate in design and code reviews, learn from feedback, and contribute to team standards for quality, safety, and trust
• Design and implement AI systems for conversational search in medical content
• Evaluate, debug, and improve conversational AI to ensure accuracy and efficiency
• Monitor and optimize system performance for latency, cost, and reliability
• Explore and apply reinforcement learning and self-improving techniques
• Collaborate and communicate effectively with cross-functional teams and stakeholders to deliver scalable solutions
• Ensure responsible and ethical AI practices throughout all stages of development
• Stay up-to-date with advances in AI/ML and actively contribute ideas, research, or solutions to improve team and product capabilities
• Collaborate with research, engineering, and product teams to translate cutting-edge AI advancements into production-ready capabilities. Uphold ethical AI principles by embedding fairness, transparency, and accountability throughout the model development lifecycle

Qualifications

• Graduate degree in computer Science or similar fields
• 8+ years of hands-on experience in AI/ML, with a solid focus on natural language processing (NLP) and Language Models
• 8+ years of experience with libraries such as OpenAI API, Hugging Face Transformers, or similar tools for finetuning and integrating LLMs
• Experience with agentic frameworks such as demonstrated by contributing to projects involving the design of modular and scalable AI agents