Responsible for the architecture and technical leadership of the end-to-end AI ecosystem including multimodal data pipelines, LLM-based engineering copilots, agentic automation systems, and predictive models for product development monitoring.
Develop the full stack AI solution (LLMs, ML, agents, APIs, pipelines).
Design multi-agent workflows for automated monitoring, anomaly detection & reporting.
Establish LLM fine-tuning, evaluation, RAG, Semantic modelling(NLP/AI) and prompt engineering frameworks.
Integrate engineering domain data (mechanical, electrical, field, test logs).
Define MLOps, model governance, observability, and monitoring strategies.
Collaborate with product development & engineering teams to map use cases.

Requirements

Four-year or Graduate Degree in Mathematics, Statistics, Economics, Computer Science, Data Science, or any other related discipline or commensurate work experience or demonstrated competence.
Typically, has 7-10 years of work experience, preferably in data analytics, statistical modeling, machine learning, or a related field.
Programming Language/s Certification (SQL, Python, or similar)
Familiarity with mechanical/electrical engineering data formats.

Benefits

Health insurance
Dental insurance
Vision insurance
Long term/short term disability insurance
Employee assistance program
Flexible spending account
Life insurance
Generous time off policies, including; 4-12 weeks fully paid parental leave based on tenure
11 paid holidays
Additional flexible paid vacation and sick leave

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

AI architecturemultimodal data pipelinesLLM-based engineeringagentic automation systemspredictive modelsfull stack AI solutionMLOpsanomaly detectionsemantic modellingprompt engineering

Soft Skills

technical leadershipcollaborationcommunication

Certifications

Programming Language CertificationSQL CertificationPython Certification