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Tier-2 brand presence and Chennai metro increase competition, but specialized ML skills narrow applicant pool.
Requires specialized production ML and MLOps experience, limiting candidacy to ML-engineering backgrounds.
Explicit 6–9 years, mandatory ML production and MLOps expertise enforce stringent shortlisting filters.
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Translate advanced data science research into scalable, production-ready ML solutions handling millions of requests with high efficiency and low latency.
Architect and lead development of end-to-end ML pipelines and APIs from scratch, ensuring cross-functional organizational alignment.
Own full lifecycle of feature delivery including requirement gathering, deployment, monitoring, and operational intelligence systems for ML model performance.
6–9 years of experience in software engineering and machine learning development.
Degree in Computer Science, Artificial Intelligence, or related quantitative field.
Proven experience building, productionizing, and maintaining scalable Machine Learning solutions.
Work Experience Required: 6–9 years; Notice period: Not explicitly mentioned in the JD.
Strong background in building and deploying high-performance ML pipelines and low-latency APIs at enterprise scale.
Experienced with MLOps lifecycle management and operational monitoring for long-term system health.
Capable of leading technical strategy, rapid prototyping, and cross-functional alignment in complex, technical environments.