Key Responsibilities
- Design and develop machine learning systems that support intelligent, context-aware user experiences.
- Own the full machine learning lifecycle, including data processing, model development, evaluation, deployment, monitoring, and optimization.
- Translate emerging research concepts into practical solutions that can be reliably deployed in production.
- Investigate and resolve model performance issues by leveraging production telemetry, logs, and user feedback.
- Continuously improve system quality through rapid experimentation, measurement, and iterative development.
- Partner with product managers, software engineers, and research teams to deliver impactful AI capabilities.
- Provide technical leadership through code reviews, design discussions, and mentorship of fellow ML engineers.
- Balance model quality with operational considerations such as latency, scalability, reliability, safety, and infrastructure cost.
Technical Environment
- Python
- PyTorch and/or JAX
- GPU-accelerated model training and inference platforms
Background
- Demonstrated experience building and deploying machine learning solutions that serve real users in production environments.
- Understanding of the strengths, limitations, and operational challenges of modern machine learning models.
- Ability to write maintainable, production-quality software with a systems-thinking mindset.
- Proven track record of independently driving projects from concept through deployment.
- Excellent problem-solving, communication, and collaboration skills.
- Comfortable working in fast-paced environments where continuous learning and iteration are essential.
Goel Navneet License No.: 02C3423 Personnel Registration No.: R1982194
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