Lead ML Engineer

What you'll do

• Lead the design, development, and operation of scalable, enterprise-grade AI/ML architectures and systems with a strong emphasis on reliability, availability, and performance.
• Lead and mentor a team of engineers, driving technical direction, code quality, and iterative delivery of large-scale solutions.
• Partner closely with data scientists, engineers, product teams, and compliance to integrate AI/ML solutions into existing and new products.
• Own the end-to-end lifecycle of GenAI services, including LLM inference, model serving, and proxy/gateway layers that support multiple downstream applications.
• Define and uphold engineering best practices around observability, scalability, security, and cost efficiency for AI/ML platforms.
• Evaluate tools, technologies, and processes to ensure the highest quality and performance of AI/ML systems.
• Stay abreast of the latest advancements in AI/ML technologies and methodologies, and translate them into pragmatic solutions for the business.
• Ensure compliance with industry standards and best practices in AI/ML.

What's required

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 10+ years of experience in software/AI/ML engineering, with a proven track record of successful delivery of complex, production-grade systems.
• Demonstrated experience building large-scale enterprise-grade services with high reliability, availability, and observability (SLO/SLA-driven environments).
• Experience leading other engineers in delivering large-scale solutions iteratively, including setting technical vision, reviewing designs, and growing team capabilities.
• Proficiency in programming languages such as Python, Java, or C++.
• Strong understanding of distributed systems, cloud platforms (AWS/Azure/GCP), containerization (Docker/Kubernetes), and modern CI/CD practices.
• Excellent problem-solving skills and the ability to work independently and collaboratively.
• Strong communication skills, with the ability to convey complex technical concepts to non-technical stakeholders and influence across teams.
• Commitment to highest ethical standards.