Data Migration Lead

Project Role :Data Migration Lead


Project Role Description :Lead the planning and execution of data migration to ensure data is accurate, complete, and ready for use in the target system. Coordinate teams, define migration approach and controls, and manage risks to ensure a smooth and reliable transition.
Must have skills :AI & Data Solution Architecture

Good to have skills :NA
Minimum 3 year(s) of experience is required

Educational Qualification :15 years full time education

Role:AI Engineer

Role Overview
We are seeking a hands-on AI Engineer with deep expertise in Amazon Bedrock, Kiro, and Snowflake Cortex to accelerate the delivery of intelligent, data-driven capabilities on our cloud data platform.
You will be embedded within our Enterprise Data Architecture and Data Platform team - a central organization responsible for establishing enterprise-wide data and AI standards, scalable platform capabilities, and governed solutions across the firm.
A key part of this role goes beyond building solutions you will also be expected to help define reusable standards, patterns, and documentation that can be adopted by engineering teams across the firm. This is a highly practical, execution-focused role requiring proven, hands-on experience delivering AI use cases end-to-end into production.

Key Responsibilities
Design, build, and deploy AI solutions leveraging Snowflake Cortex, including Cortex LLM Functions, Cortex Analyst, Cortex Search, and Cortex Fine-Tuning capabilities
Develop and operationalize agent-based workflows and agent skills using Amazon Bedrock, Kiro, and Snowflake-native tooling, integrating structured and unstructured enterprise data sources
Build and maintain AI-enabled pipelines and retrieval capabilities over enterprise data assets using Snowflake and AWS-native services where appropriate
Implement Snowflake ML features including ML-powered forecasting, anomaly detection, and classification functions within the platform
Design and author enterprise-wide AI standards and reusable patterns, ensuring they are documented in a clear, structured, and approachable format that engineers of varying experience levels can follow and implement independently
Write and maintain evaluation (eval) frameworks to assess AI output quality, including LLM-as-judge patterns, task-specific metrics, and regression testing strategies to ensure reliability and consistency of AI solutions in production
Collaborate with data engineers to ensure AI solutions are well-integrated with existing Medallion architecture (Bronze/Silver/Gold) data models and platform workflows
Integrate AI capabilities with enterprise applications and APIs, ensuring solutions are secure, governed, and production-ready
Apply best practices for AI governance, observability, and lifecycle management, including prompt versioning, model monitoring, and output quality evaluation
Contribute to reusable frameworks, reference implementations, and best practice guides that accelerate AI adoption across engineering teams firm-wide
Leverage AI-assisted development tools such as Amazon Q and Kiro to accelerate solution delivery
Required Skills
Expert-level, hands-on experience with Snowflake Cortex, including LLM Functions, Cortex Analyst, Cortex Search, and Cortex Fine-Tuning
Strong hands-on experience with Amazon Bedrock and familiarity with building AI solutions using Bedrock foundation models, orchestration patterns, and agent capabilities
Strong familiarity with Kiro and its use in accelerating AI solution development workflows
Proven experience delivering at least one end-to-end AI use case into production using Snowflake Cortex, Amazon Bedrock, or similar enterprise AI platforms
Strong Python development skills, including building modular, testable, and production-grade code for AI and data engineering use cases
Experience building and integrating agent-based workflows and agent skills, including orchestration, tool usage, prompt design, and enterprise integration patterns
Demonstrated ability to write and implement AI evaluation frameworks, including designing evals for accuracy, relevance, groundedness, and task-specific quality metrics across LLM-powered solutions
Strong technical documentation skills, with a proven ability to produce clear, well-structured standards, how-to guides, and reference patterns that are easy for engineering teams to follow and adopt at scale
Proficiency in SQL and Snowflake platform fundamentals, including Snowpark, Streamlit in Snowflake, stages, and UDFs
Familiarity with AI governance and observability practices, including prompt management, output evaluation, and monitoring strategies
Comfortable working within a GitOps/CI-CD driven workflow, including version control, automated testing, and deployment pipelines
Familiarity with AI-assisted development tools (Amazon Q, Kiro, or similar)

Qualification15 years full time education