Grade: C3

Shift: 2 pm to 11 pm IST

Role: Individual contributor role.

Location: 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 access control, network policies, and data protection for agents operating on sensitive healthcare and commercial data within the enterprise perimeter.

Collaboration Enablement

- Multi-functional Partnership: Work closely with data scientists, commercial analysts, and collaborators across Brand, Market Access, Patient Services, and Field teams. Provide frameworks, templates, and guardrails that accelerate analytics delivery.
- Develop and Validation Based on Testing: Establish testing frameworks for both traditional ML models and agent-generated code. Design validation pipelines with automated quality gates including type checking, linting, integration tests, and contract tests.
- Documentation Release Management: Develop detailed guides, operational playbooks, and user instructions. Coordinate releases with commercial operations and IT; maintain runbooks, rollback strategies, and change tickets.

Essential Skills/Experience

- Education: Bachelor s or Master s degree in Computer Science, Data Engineering, or a related field, or equivalent experience.

- Experience: 3 6+ years in MLOps, Data Engineering, or ML Platform roles with a proven track record of deploying ML solutions at scale. At least 2+ years building complex data science or large-scale analytics solutions.

- Programming:



Proficiency in Python and SQL; familiarity with TypeScript/JavaScript or a systems language (Go, Rust). Experience with TDD, CI/CD pipelines, and code quality standards.

- CI/CD Infrastructure: Experience with CI/CD tools (e.g., GitHub Actions, Azure DevOps), containerization (Docker), and cloud infrastructure concepts.

- ML Tools: Hands-on experience with model packaging and serving frameworks (e.g., SageMaker, Databricks MLflow), experiment tracking, and model registry tools.

- Data Technologies: Proficiency with Snowflake (including Snowpark and Snowpark Container Services), distributed processing (Spark), and data orchestration (Airflow).

- AI/Agent Tools: Hands-on experience with AI coding tools (Claude Code, GitHub Copilot, Cursor, or equivalent) and Cortex AI or comparable LLM serving platforms. Working understanding of how LLMs reason about code and familiarity with prompt engineering as an engineering discipline.

- Security Compliance: Understanding of data privacy and security in healthcare; experience with secrets management, audit controls, and compliance frameworks (HIPAA, SOC2, 21 CFR Part 11).

- Systems Thinking: Ability to design for how components interact at scale across both traditional ML infrastructure and agentic AI architectures.

Desirable Skills/Experience

- Domain Experience: Knowledge of pharmaceutical commercial analytics in rare disease or specialty pharma HCP/HCO targeting, patient identification, call planning, demand forecasting, specialty pharmacy data, hub/PSP operations, and omnichannel measurement.

- Rare Disease Data Proficiency: Experience with IQVIA (LAAD, Symphony, NPA), Veeva CRM, MMIT, Model N, specialty pharmacy dispense data, claims/RWD, and EMR/EHR data in small-population, high-value-per-patient environments.

- Agent System Design: Experience designing multi-agent workflows, agent orchestration patterns, and autonomous systems for enterprise applications. Understanding of MCP (Model Context Protocol) and agent interoperability frameworks.

- Performance Scalability: Experience with high-throughput inference, batch scoring at scale, low-latency APIs, and horizontal scalability for agent workloads.

- Enterprise Integration: Experience integrating with Snowflake, Veeva, Salesforce, Microsoft 365, and ServiceNow APIs to enable end-to-end automation.

- Communication Collaboration: Excellent verbal and written communication skills; able to present complex findings to both technical and non-technical audiences. Solid orientation toward teamwork in a fast-paced, regulated environment.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Analyst - Data Engineer (Bengaluru)
🏢 Alexion Pharmaceuticals
📍 Bengaluru