• Develop and deploy machine learning solutions from initial research to production, ensuring scalability and performance in live environments.
• Ensure reliable and scalable ML infrastructure, automating deployment and monitoring processes using MLOps best practices.
• Develop high-performance model-serving APIs (FastAPI, Triton, vLLM).
• Ensure data quality, security, and compliance in ML systems.
• Maintain model registry, versioning, and CI/CD pipelines.
• Collaborate with research and engineering teams to move prototypes to production.

• Bachelor’s in Computer Science, Engineering, or a related field
• 4+ years of experience in building, deploying, and scaling machine learning models in production environments.
• Deep understanding of computer science fundamentals and a solid background in software engineering.
• Experience deploying Generative AI models in production environments.
• Strong understanding of Retrieval Augmented Generation (RAG), AI Agents, and expertise in prompt engineering techniques.
• Proven experience with the full product lifecycle, taking models from R&D to deployment in fast-paced environments
•  Strong expertise in Python and containerization (Docker, Kubernetes).

Preferred skills:

•  Experience working in a product-based company, preferably within a startup environment with early-stage technical product development.
• First-principles thinking and excellent problem-solving skills, with a proactive approach to addressing challenges.
• A self-starter mentality, with the ability to take ownership of projects from end to end and work autonomously to drive results.
• Excellent communication skills, with the ability to convey complex technical concepts and a strong customer-obsessed mindset.