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
What You Bring
- 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
Nice to Have
- 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