AI Engineer

We are seeking a highly skilled and experienced Senior AI Engineer to join our dynamic team. The ideal candidate will possess a strong product mindset, a deep understanding of data, and the ability to drive business needs and outcomes.

Location: -
• Remote - This position is remote and the candidate's location in the Greater Boston Area or the Bay Area is preferred. Industry Knowledge (Preferred): Experience in life sciences or related sectors is preferred, but not required.

Educational Qualifications: -
• Master's or Ph.D. in Computer Science, Data Science, Statistics, or a related field.

Preferred Skills: -
• Experience in life sciences or related sectors is preferred.

Responsibilities: -
• Design and Deploy Production LLM Systems: Build and optimize LLM pipelines using frameworks like LangChain/LangGraph; implement production-grade RAG systems; develop and maintain knowledge graph architectures for biological and clinical data integration.
• Software Engineering Excellence: Write clean, maintainable code following best practices; implement robust testing and deployment pipelines; collaborate effectively with the engineering team using modern development practices.
• Data Engineering: Collaborate with data engineering teams to design, build, and optimize data pipelines, ensuring efficient and scalable data flow for model development and deployment.
• Data Product Development: Partner with product teams to design and develop data-driven products that enhance healthcare outcomes. This includes prototyping, testing, and scaling machine learning models for real-world applications. Be familiar and comfortable with iterative deployment methods (e.g. Scrum).
• Model Deployment: Understand MLOps and work with engineering teams to deploy models into production environments, ensuring scalability, reliability, and compliance with healthcare regulations. AWS MLOps fluency is a plus.
• Performance Optimization: Continuously evaluate and fine-tune models to improve performance and accuracy. Utilize feedback and metrics to enhance model effectiveness.
• Collaborative Innovation: Partner with product, engineering, and business stakeholders to identify opportunities for leveraging data science in improving drug development and operational efficiency.
• Compliance: Ensure that all models comply with security, privacy, and industry-specific regulations, such as HIPAA, and maintain the highest standards of data privacy and security.
• Continuous Learning: Stay current with the latest developments in machine learning, NLP, statistical modeling, data engineering, and healthcare technologies to continuously improve and innovate our data products.

Experience: -
• 7+ years of experience in data science or machine learning roles, including developing and deploying data products
• Have strong understanding of AI/ML fundamentals as well as GenAI/LLM architectures and applications
• Proven experience in building, training, and deploying AI/ML models and data products in commercial settings.
• Hands-on expertise in leveraging LangChain or similar frameworks for tasks such as question answering, chatbot development, or workflow automation.
• Strong experience in designing and developing mathematical and statistical models.
• Solid data engineering skills, including experience with ETL processes, data pipeline design, object-oriented design, services architecture, and database design, and database management.
• Worked with modern data platforms like Databricks or Snowflake
• Used source control, Git, and/or GitHub, proficiently Experience with MLOps tools and practices for deploying and managing machine learning models.

Technical Skills: -
• Proficiency in programming languages such as Python or R
• Strong understanding of machine learning algorithms, statistical modeling, deep learning frameworks (e.g., TensorFlow, PyTorch) and NLP techniques
• Proven experience working with frameworks like LangChain to develop applications powered by large language models (LLMs).
• Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and MLOps practices. Expertise in SQL and other data engineering tools (e.g., Apache Spark, Hadoop). Familiarity with containerization and orchestration tools like Docker and Kubernetes.