Core Responsibilities:
Ownership of Project/Product Delivery
- Serve as trusted advisor by actively participating in various phases of the projects (including kick-off, methodology development, execution, and continuous improvement)
- Effectively communicate with stakeholders to provide recommendations on approach and present final outcomes
- Build deep expertise on LLM / Gen AI infrastructure and productionization capabilities
Project Management and Leadership
- Set up processes to ensure continuous high-quality delivery by the team, including but not limited to implementing best project management practices
- Support a team of highly talented professionals and implement upskilling plans as required for business need, working in close collaboration with the people leader
- Coach/mentor and develop a pipeline of ML Engineering talent
Strategic Growth and Thought Partnerships
- Strategize on key opportunities and white spaces, drive discussions, and expand outcomes
- Partner with leadership on identification and development of core ML operations capabilities, and influence cross functional partners in adoption
- Interface with cross functional partners across the organization, including data engineering, data analytics, data science, IT, and other groups to adopt efficient working models and deliver on high impact projects
- Network with industry experts and external consultants as needed to develop, test, and implement new approaches and solutions for ML Operations capabilities at LCCI
Required Skills and Expertise:
- 9+ years of relevant experience in an ML Engineering, ML Ops, or Dev Ops space (preference for experience in LLMs and/or Gen AI)
- Persuasive communication and inter-personal skills
- Strong analytical and problem-solving skills to be able to structure and solve open ended business problems (pharma experience is highly preferred)
- Demonstrated expertise in building ML/automation pipelines from scratch (Including model versioning, model and data lineage, monitoring, model hosting and deployment, model optimization, scalability, orchestration, continuous learning, automated pipelines)
- Strong knowledge of Python and PySpark. Working knowledge of R is preferred
- Strong knowledge of working tools like Docker, Kubernetes, Jenkins
- Experience in using popular MLOps frameworks like MLFlow, Kedro etc
- Knowledge of frameworks such as scikit-learn, Keras, PyTorch, Tensorflow, etc. and ability to understand tools used by data scientist
- Experience with AWS (or other) cloud services: EKS, RDS, Redshift, S3, Athena, Data Lake, Blob storage, Azure etc. is additive advantage
- Experience with data pipeline and workflow management tools: Prefect, Airflow etc.
- Experience developing new components in a scrum/agile environment
- Excellent verbal and written communication skills with the ability to effectively advocate technical solutions to data scientists, engineering teams and business audiences.
Education
- Bachelor’s/ Master’s degree in Computer Applications/ Mathematics/ Pharma or a related field from a premium college