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