AI / ML Engineer

• 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
  • 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.
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regard to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Required Qualifications:

• Graduate degree in computer Science or similar fields
• 5+ years of hands-on experience in AI/ML, with a solid focus on natural language processing (NLP) and Language Models
• 5+ 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