Primary Responsibilities:

Lead end-to-end ML projects: from problem definition through deployment and monitoring.
Build scalable training and inference systems with cost controls and observability.
Set standards for experimentation (offline tests, A/B testing, causal insights).
Drive MLOps practices: CI/CD pipelines, model registry, automated retraining, drift monitoring.
Collaborate with product/design to define ML problems and success metrics.
Mentor engineers, review code/design, ensure reliability and documentation.
Work with data engineering on feature pipelines and data quality.
Communicate tradeoffs/results to both technical and non-technical teams.
Optional areas: LLM applications (RAG, fine-tuning), recommendations, anomaly detection, forecasting.
Develop and deploy AI solutions via no-code, low-code, and advanced platforms.

Required Qualifications:

Domain expertise: recommendations, forecasting, anomaly detection, optimization, RL.
Strong coding (Python/C#) and software engineering practices.
Solid ML/statistics knowledge (supervised learning, feature engineering, evaluation, deep learning/GBMs).
Responsible AI practices (GDPR/CCPA, fairness, PII handling).
Excellent communication and ability to align stakeholders.
Mentorship or tech leadership experience; open-source/patents/publications a plus.
Data engineering (SQL, Spark, pipelines).
LLMOps (prompt engineering, RAG, fine-tuning, evaluation, guardrails).
Strong MLOps background (CI/CD, containers, Kubernetes, model monitoring).
Experimentation expertise (A/B testing, guardrail metrics, causal inference).

Salary range: $110,200 – $188,800/year (based on experience and location).
Comprehensive benefits, recognition programs, equity stock purchase, and 401k contribution.
Subject to eligibility requirements.