Senior AI/ML Engineer - Remote (& only)

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. An 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.

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 HomeServe 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 practice.