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.