KEY TECHNICAL SKILLS

• University degree (Bachelor, Master or PhD) in Computer Science, Information Systems, Software Engineering, or another quantitative field
• Minimum 3 years of experience in building, deploying, monitoring and maintaining AI/ML applications utilizing cloud technologies (e.g., AWS, GCP, Azure, Snowflake etc.).
• Ability to work across the full stack and move fluidly between programming languages (e.g., Python) and MLOps technologies (e.g. Github, Argo, Metaflow, WandB, Snowflake)
• Experience in implementing CI/CD pipelines for AI/ML driven app development
• Experience in building and deploying apps with large scale data and ML pipelines
• Ability to assess new technologies and compile architecture decision records (ADRs)
• Experience working in an agile team supporting and working with cross-functional teams
• Knowledge of relational (e.g., Postgres SQL) and non-relational databases (e.g., Graph database, Vector Database etc.)
• Experience in visualization technologies (e.g. Python DASH, Tableau, PowerBI)
• Experience in developing and maintaining backend APIs (e.g. REST)
• Experience in provisioning cloud infrastructure leveraging Infrastructure as a code tools (e.g.: Docker, Kubernetes, Terraform)
• Experience in cloud-based ML engineering in an industrial setting within a global organization (technology company preferred)
• Experience in working within compliance (e.g. quality, regulatory - data privacy, GxP, SOX) and knowledge in cybersecurity is a plus

SOFT SKILLS:

• Excellent communication skills in English (both verbal and written as your work will require daily communication with teams around the globe).
• Structured, goal-oriented, and highly motivated.
• Able to work in a fast-paced, constantly evolving environment and manage multiple priorities.
• Interpersonal skills to work with technical leaders to define and enhance standards of development in AI engineering.

• Work in an agile development pod to design, build and maintain cloud hosted AI/ML products with automated pipelines that run, monitor, and retrain ML Models

• Implement automated ML pipelines (e.g., training and inference) working closely with other peers e.g., data scientists and data engineers

• Support life cycle management of deployed AI/ML apps (e.g., new releases, change management, monitoring and troubleshooting)
• Work as intermediate level subject matter expert in MLOps (e.g., develop and maintain enterprise standards, user guides, release notes, FAQs)
• Build processes and reusable components supporting seamless ML Operations (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 app userbase to develop education and communication content as per life cycle events
• Research and gain expertise on emerging tools and technologies. An enthusiasm to ask questions and try and learn new things is essential