Job Title: Analyst - Data Engineer Grade : C3 Shift: 2 pm to 11 pm IST Role: Individual contributor role. Location: Manyata Tech Park, Bangalore. Introduction to role: Are you ready to dive into the world of commercial analytics and make a real impact? Data Engineer or MLOps & AI Engineer is an incredible opportunity within the Data Science & Advanced Analytics team to support the transformation of AI/ML for Alexion’s Rare Disease Unit by crafting, developing, and fielding data science solutions that drive impact for patients.
This role works between traditional ML engineering and autonomous AI. It involves crafting, deploying, and managing classical machine learning systems and AI workflows that improve commercial efficiency across the US rare disease portfolio. The primary focus will be collaborating with data scientists, and insights & analytics to build production-grade analytics infrastructure on Snowflake and Cortex AI — from predictive patient identification models and field force alert engines to agentic workflows that autonomously surface insights and recommend actions.
This position ensures that ML models and AI agent systems are reproducible, compliant, performant, and scalable throughout their lifecycle. A strong focus is placed on data quality, monitoring, governance, and agent-executable system design. . Accountabilities: ML & Data Engineering Model Lifecycle Management: Develop and maintain pipelines to transition models from experimentation to production, including packaging, CI/CD, automated testing, and deployment.
Support model serving for patient identification, adherence prediction, Next-Best-Action engines, and competitive intelligence models on Snowflake and Cortex AI. Data Pipeline Development: Design robust batch and streaming data workflows integrating specialty pharmacy, hub/PSP, CRM (Veeva), syndicated (IQVIA, MMIT), claims, and Model N data within Snowflake. Define and manage feature sets, lineage, and reuse to support AI/ML initiatives across the rare disease portfolio.
Production Operations & Monitoring: Ensure reliability and scalability of ML systems; implement effective logging, tracing, and alerting. Establish monitoring for model performance, data drift, bias, and service health. Monitor data quality across rare disease data feeds where small population sizes amplify the impact of anomalies. Agentic AI & Agent Systems Engineering Agent Workflow Design: Collaborate with data scientists and commercial stakeholders to decompose complex business workflows into agent-executable workstreams on Cortex AI.
Determine which components are best suited for agent execution versus human data science judgment and define the boundaries between them. Instruction Architecture & Prompt Engineering: Design and maintain prompt architectures, agent skills, agent memories, and context injection patterns. Author structured coding instructions that translate commercial analytics requirements into precise agent directives with clear acceptance criteria.
Agentic Dashboard & Workflow Development: Build agentic AI systems that autonomously detect anomalies in commercial data (competitive switching, patient discontinuation signals, payer access changes), generate hypotheses, and push recommended actions to stakeholders and CRM systems. Token Economics & Cost Optimization: Optimize agent execution for cost efficiency — manage context window utilization, minimize token consumption, and design instruction patterns that reduce iteration cycles.
Monitor token economics per workstream to balance capability with budget. Governance, Security & Compliance Model, Agent & Data Governance: Implement version control, approvals, documentation, and audit trails for datasets, code, models, and agent instructions. Ensure all AI/ML outputs are explainable, auditable, and compliant with HIPAA/PHI, GDPR, FDA promotional regulations, and REMS requirements.
Enforce secrets management, role-based ac