Lead Consultant - Snowflake and Cortex AI Engineer
AstraZeneca2 hours ago
Chennai, IndiaStaff+
Responsibilities
- Define end-to-end Snowflake-native ML platform architecture using Snowpark, Snowpark Container Services, Model Registry, and Cortex AI.
- Build pipelines that move models from experimentation through production, including packaging, versioning, deployment, inference, and monitoring.
- Engineer resilient batch and streaming data workflows, feature stores, lineage, and reusable features.
- Design and deliver agentic AI systems, including agent workflows, skills, memories, semantic models, prompt architectures, context injection, guardrails, and human-oversight boundaries.
- Build autonomous detection and recommendation workflows that validate hypotheses in Snowflake and route actions to CRM and business partners.
- Optimize model, warehouse, query, compute, context-window, token, caching, and consumption costs.
- Implement logging, tracing, alerting, health monitoring, model-performance monitoring, drift monitoring, and bias monitoring.
- Apply governance, security, compliance, secrets management, access controls, masking, audit trails, approvals, documentation, and lineage.
- Lead design reviews, mentor engineers, define engineering standards, and provide reusable Snowflake-native templates and guardrails.
- Establish testing and validation pipelines covering type checks, linting, integration tests, contract tests, model validation, agent-generated code, and data-quality assertions.
- Maintain architectural records, runbooks, and change-management processes for reliable releases.
- Partner with data scientists and commercial teams to translate analytical requirements into production solutions.
Requirements
- Bachelor’s or master’s degree in computer science, data engineering, or a related field, or equivalent professional experience.
- 6–10+ years of experience in data engineering, MLOps, or ML platform roles, including architecting and deploying ML solutions at scale.
- At least 3+ years of experience building advanced data science or large-scale analytics solutions specifically on Snowflake.
- Deep Snowflake expertise across Snowpark, Snowpark Container Services, Dynamic Tables, Streams, Tasks, Snowpipe, Model Registry, UDFs, UDTFs, Stored Procedures, event tables, and performance optimization.
- Strong proficiency with Snowflake Cortex AI services, including Cortex LLM functions, Cortex Analyst, Cortex Search, Cortex Fine-Tuning, and embedding generation.
- Proficiency in Python and SQL, with familiarity in TypeScript, JavaScript, Go, or Rust.
- Experience with TDD, CI/CD pipelines, code-quality standards, Docker, Terraform, Schemachange, or SnowCLI.
- Experience with GitHub Actions or Azure DevOps and Snowflake-centric infrastructure-as-code practices.
- Practical knowledge of experiment tracking, model serving, and tools such as MLflow or SageMaker.
- Hands-on experience with AI coding tools such as Claude Code, GitHub Copilot, or Cursor and strong prompt-engineering practices.
- Understanding of healthcare data privacy and security, including RBAC, row access policies, dynamic data masking, network policies, secrets management, and audit controls.
- Experience designing scalable interactions across traditional ML infrastructure and AI systems, preferably with Snowflake-native architectures.
- Preferred experience in pharmaceutical commercial analytics, rare disease or specialty pharma, multi-agent workflows, MCP, tool-use patterns, and agent interoperability frameworks.
- Preferred experience with Snowflake Data Sharing, Snowflake Marketplace, Iceberg Tables, hybrid tables, cross-cloud replication, high-throughput inference, batch scoring, external functions, and horizontal agent scalability.
- Preferred experience integrating Snowflake with Veeva, Salesforce, Microsoft 365, and ServiceNow APIs.
- SnowPro Advanced Data Engineer, SnowPro Advanced Architect, or equivalent certification is desirable.
- Excellent communication, architecture presentation, mentoring, collaboration, and teamwork skills in a fast-paced regulated environment.
Benefits
- Hybrid work arrangement with an expectation of working from the office an average minimum of three days per week, while allowing individual flexibility.
- Inclusive and diverse workplace with equal opportunity and consideration for qualified applicants.
- Opportunity to work on data and AI systems supporting life-changing medicines and rare-disease commercial outcomes.
Tech Stack
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About AstraZeneca
We're transforming the future of healthcare by unlocking the power of what science can do for people, society and the planet. For more information, visit www.astrazeneca.com. Community Guidelines: bit.ly/2MgAcio