Job Description

• 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