What are the responsibilities and job description for the AI/ML Lead Engineer - W2 Position position at Tech Rakers?
AI / ML Lead Engineer Hybrid 4 days/week Minneapolis, MN 12mo Contract W2 Position
Role Summary
This role leads the technical execution of AI delivery while remaining deeply handson.
The lead ensures the team builds solutions that are scalable, secure, and productionready.
Business Challenge This Role Solves
Accelerates delivery on a greenfield AI program
Reduces technical risk and rework
Aligns business needs with technical execution
Core Responsibilities
Design endtoend AI solutions from requirements to production
Mentor and guide AI/ML engineers on best practices
Break business problems into executable technical work
Review architecture, code, and deployment approaches
Partner closely with business stakeholders and program leadership
Ensure AI solutions meet security, compliance, and reliability standards
Required Technical Experience
7 to 10 years of software engineering experience
Proven delivery of AI solutions into production environments
Experience leading or mentoring engineers
Strong background in cloud, APIs, and enterprise systems
Comfortable working directly with business partners
Top Skills
Endtoend AI solution architecture
Technical leadership and mentorship
Production deployment
Communication and collaboration
Additional Technical Skills
Sprint planning and estimation
Risk assessment and mitigation
Documentation and standards definition
Stakeholder management
Top Skills Details
- EndtoEnd AI Solution Design: Designing AI solutions from business requirements, Defining architecture, patterns, and standards, Evaluating tradeoffs between speed, risk, and scale
- Deep Production Experience: Proven experience deploying AI into enterprise environments, Understanding operational risks and mitigations, Knowing what breaks in production and why
- Technical Leadership and Mentorship: Guiding engineers on architecture and implementation, Reviewing designs and code, Helping engineers ramp up on deployment practices
- Business Communication: Translating business problems into technical plans, Explaining AI behavior to nontechnical stakeholders, Aligning delivery with business priorities
- Experience deploying production ready solutions in financial services or similar highly regulated industry
Interview Information
1: Hiring manager screen
2: Technical interview (handson or scenariobased), Deep technical panel with engineers,
3. Optional coding round if needed
Focus is on:
Realworld problem solving
Production experience
Ability to explain AI decisions clearly
Business Challenge
Ensures AI solutions are scalable, reliable, and aligned to business priorities. Accelerates adoption while reducing risk and rework.