Head of Machine Learning Engineering Operations

Key Responsibilities:

• Work closely with the respective managers across the data organization to provide the right level of resources and skillsets to deliver AI products.
• Hire and build a high performing MLOps team.
• Lead client's Client development standards and best practices.
• Collaborate with others in engineering and product leadership to create and own the long-term roadmap and deliverables for your team.
• Effectively communicate the roadmap and strategy within the team and across the organization.
• Create and manage processes that enable team members to do their best work.
• Develop assets, accelerators, and thought capital for your practice by providing best in class framework and reusable components.
• Work in agile pods to design and build cloud hosted, Client products with automated pipelines that run, monitor, and retrain Client Models.
• Oversee the design of AI/Client apps and the implementation of automated model and pipeline adaption.
• Validation working closely with solution architects, data scientists and data engineers leads/managers.
• Bring your deep expertise in cloud architecture/DevOps to analyze and recommend enterprise-grade solutions for operationalizing AI / Client analytics.
• Lead development end-to-end (Data/Dev/Client) Ops pipelines based on in-depth understanding of cloud platforms, AI lifecycle, and business problems to ensure analytics solutions are delivered efficiently, predictably, and sustainably.
• Researching and gain expertise on emerging tools and technologies. An enthusiasm to ask questions and try and learn new things is essential. Prototype and demonstrate solutions. Be innovative.
• Use your judgment to craft solutions to complex problems or seek guidance as needed.
• Support life cycle management of deployed Client apps (e.g., new releases, change management, monitoring and troubleshooting).
• Work as MLOps subject matter expert (e.g., develop and maintain enterprise standards, user guides, release notes, FAQs). Build processes supporting seamless MLOps (e.g., app monitoring, troubleshooting, life cycle management and customer support).
• Walk stakeholders and solution partners through solutions and reviewing product change and development needs.
• Maintain effective relationships with stakeholders to develop education and communication content as per life cycle events.
• Drive impact by leading your teams to deliver results with sustainable engineering practices.
• Co-Own project planning and execution of multiple features and releases.
• Contribute to architecture, design, and code reviews. Also not be afraid to do some hands-on development/debugging to solve complex use cases.
• Lead a team of high-performing engineers and tech leads. Help them grow their skillset through hands-on experience, mentorship, and feedback.
• Nurture a culture of excellence on the team through hiring diverse and talented engineers, predictable execution, and a high level of quality.
• Participate to the engineering community and be a trusted advisor to your colleagues and to our stakeholders.

Key Requirements:

• 10+ years of relevant experience.
• Demonstrated Design-to-implementation consulting experience across digital and the establishment of engineering functions.
• Demonstrated use of technologies to develop, monitor and deploy products (e.g. Github, GitAction, Splunk, cloudwatch, Argo, Spark, MLflow, ...).
• Demonstrated ability in the establishing of MLOps practice and associated practices in complex environments.
• Experience in coaching and leading agile dev teams, and building a team and culture around agile development practices.
• Demonstrated experience in building AI scalable industrialized products, establishing the vision for major products.
• Experience interacting and influencing cross-functional stakeholders to execute high-impact projects.
• Demonstrated leadership and coaching skills to develop individuals and teams.
• Experience in data science, statistics, software engineering, modular design and design thinking.
• Experience developing CI/CD pipelines for AI/Client development, deploying models to production, and managing the lifecycle in a regulated environment.
• Experience building and deploying data science apps with large scale data and Client pipelines and architectures.
• Strong understanding of Client and AI concepts and hands-on experience in development, deployment and agile life cycle management of data science apps (MLOps).
• Ability to assess new technologies and compile architecture decision records (ADRs).
• Excellent communication skills in English, both verbal and in writing.

Required Skills and Certifications:

• MLOps
• Github
• GitAction
• Splunk
• cloudwatch,
• Argo
• Spark
• MLflow