Machine Learning Engineer

Were looking for a Machine Learning Engineer to join our Applied AI Lab and play a hands-on role in building intelligent, scalable, and production-ready ML systems. Youll work at the intersection of rapid prototyping and product-focused ML, contributing to both short-cycle innovation and long-term platform stability.

If you enjoy wearing multiple hats, thrive in fast-paced environments, and are excited about shaping how AI transforms healthcare and life sciences, this opportunity is for you.

• Solve business problems with cutting-edge AI solutions, focusing on cost-efficiency and reliability.
• Collaborate with Lab leads, product designers, researchers, and architects to scope MVPs and define core capabilities.
• Build modular, reusable components for GenAI solutions and traditional ML pipelines (training, evaluation, deployment).
• Design and develop API-based AI tools and batch data transformation workflows.
• Implement end-to-end solutions for batch and real-time algorithms, including monitoring, logging, automated testing, and performance evaluation.
• Develop production-grade solutions following software engineering best practices.
• Support experimentation, A/B testing, and fine-tuning cycles.
• Stay updated on the latest advancements in Generative AI and related technologies.
• Document architectures, processes, and technical decisions.

• Bachelors/Masters in Computer Science, Engineering, Data Science, or related field.
• 4.5+ years of experience as an AI/ML Engineer.
• Strong knowledge of Python, SQL, and data processing languages.
• Solid understanding of ML concepts (GenAI, NLP, LLMs).
• Hands-on experience with open-source LLMs.
• Proficiency in software engineering fundamentals (unit testing, modular design, CI/CD).
• Strong experience with ML/AI frameworks (scikit-learn, PyTorch, TensorFlow, HuggingFace).
• Familiarity with cloud platforms (AWS/Azure), containerization (Docker), and version control (Git).
• Exposure to ETL tools (Spark, Airflow), PySpark, data modeling/warehousing, and CI/CD pipelines is a plus.
• Experience with vector embeddings, multimodal data, agentic workflows, and LLM fine-tuning/RAG pipelines.
• Exposure to healthcare/life sciences datasets (EHR, claims, clinical trials).
• Prior experience in startup or R&D environments preferred.
• Excellent communication and collaboration skills.