Machine Learning/Artificial Intelligence Ops Lead
Indegene is on a mission to empower every healthcare enterprise in its transformation journey. We are uniquely positioned to solve the challenges of the healthcare industry through our combination of deep medical knowledge and verticalized technology to help make healthcare affordable and accessible to every person on the planet.
We are innovators at heart and real innovation for us means constantly obsessing over what will drive success for our customers in the future. No wonder 18 of the top 20 global biopharma organizations lean on us for an agile and enduring partnership. With close to 5000 brilliant employees spread across the US, Europe, China, Japan, and India, we have delivered over 100 strategic engagements and fully commercialized a $2B+portfolio
We have brought together a group of incredibly passionate and brilliant people and entrusted them to solve some of healthcare’s most challenging problems, which has led to our culture being centered on passion, innovation, and collaboration. We’re passionate about healthcare; we believe that technology and innovation will carve an indomitable path in transforming healthcare for the better, and to achieve this, we collaborate with the best minds in the medical and technological domains.
Indegene is focused on helping customers drive Customer Experience by unlocking the value of Enterprise data. This drives the charter for our data management team including Data Strategy, Data Governance, Data Quality, Data Operations, Data Platforms, and Data Literacy. The team’s mission is to enable the right data, at the right time, with the right controls compliant with Data Policies & Standards, centralized tools and capabilities, while ensuring data is high quality, discoverable, and accessible when needed.
- Ability to work in a fast paced start up mindset. Should be able to manage all aspects of AI/ML activities
- Develop platforms that make data across applications/application deployments available for AI/ML-driven feature prototyping, proofs-of-concept, and general availability
- Refine ML pipelines for analysis, while refining, automating, and scaling as needed for the use-case at hand
- Work on various aspects of the AI/ML ecosystem - model building, ML pipelines, logging & versioning, documentation, scaling, deployment, monitoring and maintenance etc.
- Work closely with Data scientists and MLOps Engineers to come up with scalable system and model architectures for enabling real-time ML/AI services
- Create consumable services utilizing caching and other strategies to maximize performance
- Masters or equivalent experience in Informatics, CS, Data Science or a related field
- 5+ years of experience as a data engineer or data scientist with a focus on data and ML infrastructure engineering
- Knowledge on different digital channel datasets and omni-modeling experience is mandatory
- Experience in pharma domain is preferred
- Strong Python and SQL coding skills
- Experience load-testing, profiling, maximizing performance, and choosing the tool stack that provides the right price-performance fit
- Experience with AWS Data and ML Technologies (AWS Sagemaker, Data Pipeline, Glue, Athena, Redshift etc)
- Experience working on datasets involving project management, software development, and resource planning
- Demonstrated experience building data and analytics pipelines/services that efficiently scale for cloud application usage, meeting a product team’s SLA for performance and resilience
- Experience with common libraries and frameworks in data science (Scikit Learn, Tensorflow, PyTorch etc)
- Experience with automated deployment and orchestration (CI/CD, Github Actions, Docker etc)
- Experience with data version control (DVC), orchestration tools (Kubeflow, etc), and MLOps tools (AWS SageMaker Experiments, SageMaker Monitoring, MLflow, Seldon, KServe etc)
- Skilled at working as part of a global, diverse workforce of high-performing individuals
- AWS Certification is a plus