Back
Tier-1 brand, mid-level ML role, metro location, and broad skillset increase applicant competition.
Production ML engineering skills are moderately transferable but favor candidates with lifecycle and MLOps experience.
Explicit 6–9 years plus mandatory production ML and MLOps skills make hiring filters strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Translate advanced data science models into scalable, production-grade ML software handling millions of requests with low latency.
Architect and lead the development of end-to-end machine learning pipelines including deployment, monitoring, and feedback loops.
Own full feature lifecycle from requirements gathering to deployment, and drive cross-functional technical alignment and POC initiatives.
6–9 years professional experience in software engineering and machine learning development.
Degree in Computer Science, Artificial Intelligence, or related quantitative field.
Proven experience building and maintaining production ML solutions at scale.
Not explicitly mentioned in the JD: notice period and location constraints.
Experienced in production ML engineering with a strong background in translating complex models into efficient software.
Demonstrated technical leadership skills in ML architecture, rapid prototyping, and cross-functional collaboration.
Strong expertise in MLOps, scalable pipeline design, and operational monitoring frameworks.