Job description

What You’ll Do

Architect and deliver scalable platform capabilities to support machine learning and AI workloads across multiple use cases
Partner with data scientists to translate modeling needs into performant infrastructure and production-ready systems
Build and manage end-to-end pipelines for data ingestion, transformation, model deployment, and monitoring
Establish best practices for model lifecycle management, including versioning, testing, and observability
Develop backend services and APIs using modern programming languages (e.g., Python, Java) and support lightweight UI integrations where needed
Design and optimize data storage solutions across relational, non-relational, and graph-based systems
Integrate platform components with messaging systems, identity providers, and external services
Ensure system performance, scalability, and uptime through proactive monitoring, tuning, and incident response
Automate operational processes to improve platform efficiency and reduce manual overhead
Contribute to architectural decisions around distributed systems and microservices-based environments
Provide technical mentorship and guidance to engineering teams leveraging the AI platform
Maintain clear documentation of system architecture, workflows, and operational procedures
Stay informed on emerging trends in AI/ML and incorporate relevant advancements into the platform strategy

10+ years of experience building and scaling complex software or data platforms, including leadership of large initiatives
Demonstrated experience supporting AI/ML systems in production environments
Strong programming skills in one or more languages such as Python or Java
Experience with cloud-native architectures (e.g., AWS, Azure) and container orchestration (e.g., Kubernetes, Docker)
Deep understanding of data systems, including SQL and NoSQL databases, as well as modern data architectures
Familiarity with vector-based and graph-based data stores, caching layers, and high-throughput data processing systems
Experience building APIs and working with modern web architectures (REST, API gateways, SPAs)
Knowledge of machine learning frameworks such as TensorFlow, PyTorch, or similar tools
Hands-on experience working with large-scale data platforms and distributed processing technologies
Strong grasp of software engineering fundamentals, including data structures, algorithms, and design patterns
Experience with version control, CI/CD pipelines, and Agile development practices
Understanding of data governance, security, and privacy considerations (e.g., encryption, data protection strategies)
Strong problem-solving abilities and the ability to operate effectively in fast-paced environments
Excellent communication skills with the ability to collaborate across technical and non-technical teams

Advanced degree in a technical field such as computer science, AI, or data science
Exposure to emerging areas such as generative AI, reinforcement learning, or autonomous systems
Experience with MLOps tools and frameworks
Background deploying ML models at scale in production environments
Familiarity with domain-specific applications of AI (e.g., regulated industries)
Prior hands-on experience as a data scientist or in applied machine learning roles
Understanding of distributed system design and microservices architecture

Principal AI Engineer 0426

Role overview