Need - Senior AI Engineer with AWS Data Lake Migration
Contract Length - 1 year contract on W2
Visa's - or USC
Location - Remote
Full Job Description
Job Description:
AWS Data Lake Migration Project Summary
Objective
Migrate all Client systems and databases into an AWS data lake, beginning with a raw "bronze layer" lift-and-shift from core systems.
Scope & Phasing
- Ultimate goal: Move data from 100+ source systems to AWS.
- Initial focus: Narrowed to the top 5 priority systems (e.g., CRM, ERP, others).
- Approach:
- First step = raw ingestion (bronze layer).
- Transformations and refinements to follow later.
- Timeline:
- High-priority deliverables by end of June.
- Overall initiative expected to run at least 1 year.
Current Challenges
- Data Access & Scale
- Difficulty gaining access to source systems.
- Large, complex datasets.
- Systems spread across:
- Cloud platforms
- Client data centers
- Third-party-managed environments
- Tooling & Architecture
- Case-by-case migration strategy.
- Potential use of AWS Glue and other ETL tools.
- No single standardized method yet.
- Organizational Complexity
- Automation team (25 people) originally attempted to use AI/agentic AI to accelerate migration-did not succeed.
- Initiative now transitioning to the Data team.
- Data team currently focused on reporting and analytics.
- Only 3 4 AI engineers; unclear if leadership and skillsets are sufficient for the scale of effort.
- Leadership concerns and need for stronger technical direction.
- Stakeholder Impact
- Many business groups impacted.
- Critical to project confidence and execution credibility across the organization.
- "Must deliver" environment-technology execution is the top priority.
Key Risks
- Lack of strong technical leadership.
- Access bottlenecks to source systems.
- Inconsistent tooling approach.
- Skill gap in AI/data engineering leadership.
- Compressed near-term deadlines.
Core Need
- Strong technical leadership to own execution.
- Clear architecture and migration framework.
- Prioritized roadmap for top systems.
- Improved governance and stakeholder communication.
Shift from experimental AI-led automation to structured data engineering pract
Formal JD
A Client USA AI/ML Engineer identifies, develops, and scales generative AI technologies to transform business operations. The role focuses on piloting proof-of-concept (POC) solutions, collaborating with product teams, and deploying production-ready AI applications using Python, Node.js, or Java to enhance service efficiency and customer experience.
Key Responsibilities
- Generative AI Development: Lead pilot projects, create prototypes, and deploy AI solutions from concept to production.
- Prompt Engineering & Optimization: Refine prompts for Generative AI, using analytics to optimize performance.
- System Integration: Utilize Cloud Services (APIs, Cloud Functions) and write high-quality code in Python, Node.js, or Java.
- Business Collaboration: Work with Marketing, Sales, and Product teams to identify use cases for automation, personalization, and efficiency.
- Technical Strategy: Monitor the impact of AI capabilities on business metrics and stay updated on AI advancements.
Required Skills and Qualifications
- Experience: Generally requires 3+ years of experience in AI/ML development, particularly with Generative AI technologies.
- Technical Proficiency: Strong programming skills in Python, Java, or Node.js.
- AI/ML Knowledge: Expertise in machine learning, NLP, or deep learning, and familiarity with AI frameworks.
- Cloud Platforms: Experience with AWS, Azure, or Google Cloud Platform.
Communication: Ability to collaborate with cross-functional teams to translate business needs into technical requirements.
Ayush Sharma Sr. US Technical Recruiter
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