Project Lead

Greetings!

Senior AI Engineer Supply Chain TechM210130

Austin, TX --Day1

for subcon 65-70/hr

Locals are Preferred

Senior AI Engineer Supply Chain

Hands-on execution of AI projects across the supply chain Outcome-driven

Role Summary

The Senior AI Engineer is the hands-on technical lead responsible for executing AI and automation projects across the supply chain. You will own technical delivery end to end: framing the solution, assembling the data, building and validating models or agents, integrating with business systems, and partnering with platform teams to hand off into production. Success is measured by adopted, working AI capabilities that produce measurable business outcomes.

A portfolio of AI projects delivered across the supply chain with quantified business impact (cycle time, accuracy, cost, planner productivity, or service level)

Multiple capabilities transitioned into production with clean hand-off to platform and operations teams

Reusable data foundations, components, and evaluation frameworks that accelerate subsequent projects

Trusted technical partner to supply chain business owners and to platform / MLOps teams

Key Responsibilities

Translate supply chain problems (forecasting, supplier risk, planner copilots, exception triage, document extraction, scheduling, optimization, and similar) into well-scoped AI projects with explicit success criteria

Own end-to-end technical execution: solution design, data assembly, model or agent build, integration, validation, and production hand-off

Stand up and maintain data foundations across ERP, MES, PLM, supplier portals, and external signals; profile, clean, and document quality and lineage

Select the right approach for each problem rules, heuristics, optimization, time-series, classical ML, LLMs, RAG, or agents and combine them where it produces the best outcome

Establish deterministic baselines and quantify AI lift with appropriate statistical rigor; validate continuously, not only at project start

Build, deploy, and iterate on solutions with real users; instrument adoption, outcomes, and failure modes

Harden code, data pipelines, and integrations for broader rollout across users, sites, or business units

Partner with platform, data, and MLOps teams on deployment patterns, monitoring, alerting, and operational hand-off

Run multiple concurrent projects by leveraging reusable scaffolding, evaluation harnesses, and templates

Communicate clearly to technical and business audiences including executive-ready status, evidence, and recommendations

Deliverables

Solution design notes covering problem, approach, data, assumptions, risks, and integration points

Data foundation documentation sources, lineage, quality, and gaps

Working AI capabilities deployed to real users, with quantified business impact

Validation and evaluation reports metrics, baselines, failure modes, sensitivity analysis

Production-ready code, pipelines, and integration artifacts

Operational hand-off packages for platform / MLOps teams (monitoring playbooks, runbooks, dependencies)

Reusable patterns, components, and frameworks that benefit future projects

Required Qualifications

7+ years in AI/ML or data engineering, with a track record of shipping AI capabilities into production not only prototypes

Strong Python and SQL; comfort across the data $B"*(B model $B"*(B integration $B"*(B output stack

Hands-on experience with at least two of: LLMs/agents, forecasting, optimization, classical ML, document/NLP extraction

Demonstrated ability to take AI systems from concept through pilot to production, including hardening, integration, and operational hand-off

Skill in working with ambiguous problems, imperfect and evolving enterprise data, and shifting requirements

Excellent written and verbal communication; able to defend results to both skeptical technical reviewers and senior business stakeholders

Preferred Qualifications

Prior supply chain, manufacturing, or operations exposure in the semiconductor or high-tech industries

Experience with ERP, MES, and PLM data structures (SAP, Oracle, Kinaxis, or similar)

Familiarity with LLM evaluation, RAG patterns, agent frameworks, and vector stores

Experience building hybrid systems that combine rules, heuristics, and AI

History of scaling AI across multiple sites, users, or business units

Experience shaping reusable AI delivery frameworks, components, or internal platforms

The pay range for this role is $110k - $120k per annum including any bonuses or variable pay. Tech Mahindra also offers benefits like medical, vision, dental, life, disability insurance and paid time off (including holidays, parental leave, and sick leave, as required by law). Ask our recruiters for more details on our Benefits package. The exact offer terms will depend on the skill level, educational qualifications, experience, and location of the candidate.

Thanks

Regards

Tech Mahindra is an Equal Employment Opportunity employer. We promote and support a diverse workforce at all levels of the company. All qualified applicants will receive consideration for employment without regard to race, religion, color, sex, age, national origin or disability. All applicants will be evaluated solely on the basis of their ability, competence, and performance of the essential functions of their positions with or without reasonable accommodations. Reasonable accommodations also are available in the hiring process for applicants with disabilities.

Candidates can request a reasonable accommodation by contacting the company ADA Coordinator at