onsite
Senior Machine Learning Engineer III - LexisNexis
ML Engineer
Lead the design, implementation, and scaling of AI/ML and LLM solutions for legal products, owning system architecture, infrastructure, and production pipelines while mentoring a team of engineers.
About the role
Key Responsibilities
- Architect, develop, and deploy end‑to‑end machine learning and large language model pipelines for legal‑tech applications.
- Own the full production lifecycle, including data ingestion, model training, validation, monitoring, and continuous improvement.
- Design and manage cloud‑native infrastructure on AWS, leveraging Docker, Kubernetes, and CI/CD for scalable, reliable MLOps workflows.
- Collaborate with product, data, and research teams to translate business requirements into robust AI solutions.
- Mentor junior engineers, conduct code reviews, and promote best practices in model reproducibility and performance optimization.
Requirements
- 5+ years of professional experience building and deploying production ML/LLM systems.
- Strong proficiency in Python and deep‑learning frameworks such as TensorFlow or PyTorch.
- Hands‑on experience with AWS services (SageMaker, EC2, S3) and container orchestration using Docker and Kubernetes.
- Demonstrated ability to design scalable MLOps pipelines, including automated testing, monitoring, and model versioning.
- Excellent problem‑solving skills and a track record of mentoring technical teams.
Skills
pythontensorflowpytorchawsdockerkubernetesmlops