Job Summary:

We are seeking a Principal Machine Learning Engineer to help establish and lead our Machine Learning Engineering function. This role will be instrumental in shaping the future of our ML and LLM capabilities, driving innovation across traditional ML pipelines, AI/LLM applications, and ensuring fairness, scalability, and cost-efficiency in our systems.

You'll work directly with the Director of Data, AI, and ML and partner closely with engineering and product leadership to grow and mentor a team of Machine Learning Engineers. Your impact will shape both the technical direction of our ML systems and the culture of our growing team.

Duties/Responsibilities:

• Architect and lead the development of scalable ML infrastructure for training, inference, and model lifecycle management.
• Design and implement systems for ML experiment tracking, model registry, and feature store integration.
• Develop and deploy LLM-powered applications, including prompt tuning, retrieval-augmented generation (RAG), and vector database integration.
• Lead AI/LLM initiatives across the organization, including architecture and deployment of LLM-powered systems.
• Establish frameworks for bias and fairness assessment across ML models and ensure compliance with internal and external standards.
• Collaborate with data scientists, engineers, and product teams to translate business needs into ML solutions.
• Lead cost management initiatives for ML workloads, optimizing cloud resource usage and model efficiency.
• Mentor and guide a team of ML engineers working in Agile, fostering a culture of innovation, transparency, and technical excellence.
• Drive the adoption and management of big data frameworks and tools (e.g., Snowflake, ML-Flow, Airflow, dbt) to support ML scalability.
• Support analytics and client-facing teams with model explainability tools and performance reporting

Skills/Abilities/Experience:

• 7+ years of experience in Machine Learning Engineering or Applied ML, with 3+ years of leadership experience.
• Expertise in ML infrastructure, model lifecycle management, and experiment tracking tools (e.g., MLflow, Weights & Biases).
• Hands-on experience with LLMs and NLP systems, including prompt tuning, RAG architectures, and vector databases (e.g., Qdrant, Pinecone).
• Strong understanding of bias and fairness metrics, and experience implementing auditing frameworks.
• Proficiency in big data tools and cloud platforms (e.g., AWS, GCP, Azure, Snowflake, Spark).
• Strong software engineering skills in Python and experience with containerization and orchestration (e.g., Docker, Kubernetes).
• Experience with API development for supporting real-time model inference (FastAPI, OpenAPI, Docker, K8s/Nomad, etc)
• Experience managing ML costs and optimizing cloud resource usage.
• Excellent communication and collaboration skills, with a track record of cross-functional leadership.
• Passion for building scalable, transparent, and impactful ML systems.

Nice to Haves:

• • • Experience in a startup environment
    • Experience with boosted models like XGBoost
    • Experience with Optimization problems and tools (gurobi, cuOpt, etc)
    • Familiarity with Healthcare data

Physical Requirements:

• Ability to stand and sit for extended periods of time.
• Ability to lift 10 lbs.