Director - Machine Learning Engineering (13-20 yrs)

Director - Machine Learning Engineering

- We are looking for an experienced Director, Machine Learning Engineering in MLX Platform to help us build the Model Governance and Observability systems.

- In this role you will work on to build robust SDKs, platform components to collect metadata, traces and parameters of models running at scale and work on cutting edge Gen AI frameworks and their instrumentation.

- You will also lead the teams to analyze and optimize model performance, latency, and resource utilization to maintain high standards of efficiency, reliability and compliance.

- You will build and lead a highly talented software engineering team to unlock innovation, speed to market and real time processing.

- This leader must be a deep technical expert and thought leaders that help accelerate adoption of the engineering practices, up skill themselves with the industry innovations, trends and practices in Software Engineering and Machine Learning.

- Success in the role requires an innovative mind, a proven track record of delivering highly available, scalable and resilient governance and observability platforms.

What You'll Do:

- Lead, manage and grow multiple teams of product focused software engineers and managers to build and scale Machine Learning Model Governance and AI Observability platforms & SDK's

- Mentor and guide professional and technical development of engineers on your team

- Work with product leaders to define the strategy, roadmap and destination architecture

- Bring a passion to stay on top of tech trends, experiment with and learn new technologies participate in internal & external technology communities, and mentor other members of the engineering community

- Encourage innovation, implementation of state of the art ( SOTA) research technologies, inclusion, outside-of-the-box thinking, teamwork, self-organization, and diversity

- Work on cutting edge Gen AI frameworks/LLMs and provide observability using open Telemetry

- Analyze and optimize model performance, latency, and resource utilization to maintain high standards of efficiency, reliability and compliance

- Collaborate as part of a cross-functional Agile team to create and enhance software that enables state of the art, next generation big data and machine learning applications.

Basic Qualification:

- Bachelor's degree in Computer Science, Computer Engineering or a technical fie

- At least 15 years of experience programming with Python, Go, Scala, or C/C++

- At least 5 years of experience designing and building and deploying enterprise AI or ML applications or platforms.

- At least 3 years of experience implementing full lifecycle ML automation using ML Ops(scalable development to deployment of complex data science workflow

- At least 4 years of experience leading teams developing Machine Learning solutions and scaling

- At least 10 years of people management experience and experience in managing managers.

Preferred Qualification:

- Master's degree or PhD in Engineering, Computer Science, a related technical field, equivalent practical experience with a focus on modern AI techniques.

- Strong problem solving and analytical skills with the ability to work independently with ownership, and as a part of a team with a strong sense of responsibilities.

- Experience designing large-scale distributed platforms and/or systems in cloud environments such as AWS, Azure, or GCP.

- Experience architecting cloud systems for security, availability, performance, scalability, and cost.

- Experience with delivering very large models through the ML Ops life cycle from exploration to serving

- Ability to move fast in an environment with ambiguity at times, and with competing priorities and deadlines.

- Experience at tech and product-driven companies/startups preferred.

- Ability to iterate rapidly with researchers and engineers to improve a product experience while building the core platform components for Observability and Model Governance

- Experience with one or multiple areas of AI technology stack including prompt engineering, guardrails, vector databases/knowledge bases, LLM hosting, advanced RAG and fine-tuning