Back
Tier-1 brand and metro location increase applicant density, but seniority and niche ML/forecasting requirements temper competition.
Specialized forecasting, regulated biotech and supply-chain experience required, reducing cross-industry transferability.
Explicit 12+ years plus extensive mandatory ML, MLOps, and regulated-domain experience creates strict filtering.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Set technical strategy and lead architecture for enterprise forecasting and AI decision support systems integrating ML, LLM, and agentic AI technologies.
Design, build, deploy, and scale production-grade ML systems including forecasting, simulation, optimization, and autonomous workflow automation.
Collaborate cross-functionally to translate ambiguous requirements into reliable, governed AI solutions impacting critical business processes and forecasting capabilities.
12+ years of experience in machine learning engineering, software engineering, or related quantitative discipline.
10+ years building, deploying, and operating production ML or AI systems with technical leadership on complex initiatives.
Deep hands-on experience across the full ML engineering lifecycle including MLOps, model deployment, monitoring, and governance.
Mandatory skills: Python, SQL, experience with ML frameworks (scikit-learn, PyTorch, TensorFlow/JAX), cloud platforms, containerization, CI/CD, and distributed systems.
Experienced in architecting scalable, secure, and maintainable forecasting or decision-support AI systems in regulated or complex operational environments.
Proven ability to lead technical strategy and deliver cross-functional AI/ML products with measurable business impact in large matrixed organizations.
Strong collaborator able to communicate complex technical tradeoffs effectively to both technical and non-technical stakeholders including senior leadership.