Associate Director, MLOps (Staff AI / ML Engineer) - Hybrid
Indexed description
Responsibilities
Essential Areas of Responsibility
- Work with cross-functional teams to architect and implement AI products that are scalable, performant, secure, and manage technical debt
- Demonstrate ownership of the technical delivery of product end to end, from collecting business requirements to architecting, implementing, and testing a solution
- Develop and maintain AI and ML platforms to expedite the development of ML/AI products and solutions, ensuring that they meet both current and future business needs, support large-scale data and model training, and maintain performance
- Work closely with product managers, PMs, Researchers, Data Scientists, development teams, and stakeholders to align end to end technical solutions with product and business objectives
- Research state-of-the art AI solutions and approaches to address a wide variety of use cases and platform needs
- Integrate solutions across a robust enterprise infrastructure stack including Docker, Snowflake, AWS, IaS, and JFrog
- Provide guidance and mentorship to development teams, improving overall team capability with respect to software engineering, data engineering, and generative AI application development
- Contribute to a culture of belonging, respect, team-focus, individual ownership, and performance within innovation lab
- MS, PhD, or equivalent degree in related field with minimum 7y of work experience in Data Science / Machine Learning – OR – BS or equivalent degree in related field with minimum 10y or work experience in Data Science or Machine Learning
- Related degree fields include Data Science, Computer Science, Statistics, Mathematics, Engineering, Physical Sciences
- Experience in the Life Sciences industry preferred, though not required
- Expert-level proficiency in Python, including production software development
- Expert-level experience developing deploying and maintaining production software and data products using tools in orchestration (e.g. Airflow, Prefect), containerization / virtual environments (e.g. Docker), version control (e.g. Github), CI/CD (e.g. Github Actions), observability (e.g. DataDog) and Cloud Architectures (e.g. AWS, GCP)
- Proficiency in working with common Generative AI frameworks and related tools including LangGraph, Pydantic, FastAPI, Snowflake Cortex, and related
- Strong knowledge of OLAP-style columnar SQL databases (i.e. Snowflake) and data models. Strong understanding of data pipeline, ETL processes and technologies (e.g. DBT). Experience with distributed big-data frameworks such as Spark are a plus
- Experience working in agile product development methodologies, iterative and prioritization-based development using the 80-20 rule, and incorporating user feedback into technical features for development
- Ability to work in cross-functional technical teams in an enterprise environment to implement technical solutions
- Experience influencing and mentoring junior team members as an informal technical leader
- Self-starter with the ability to work with minimum supervision
- Business engagement experience with the ability to manage evolving business priorities and demands
- Ability to multitask – effectively manage simultaneous work requests across departments, IT, and other cross-functional organizations
- Willingness to learn new applications, tools, and approaches as additional needs arise
- Strong work ethic, with a proven track record in successfully achieving goals
- Proactive communication style
- Excellent verbal and written communication skills with the ability to present to and interact with a diverse group of executives, managers, and subject matter experts
- Ability to work 2-3 days a week in our Waltham office.
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