Software Architect - ML Ops at Sysco

About the role

JOB DESCRIPTION Software Architect - MLOps     The Big Picture  Sysco LABS is the Global In-House Center of Sysco Corporation (NYSE: SYY), the world’s largest foodservice company. Sysco ranks 56th in the Fortune 500 list and is the global leader in the trillion-dollar foodservice industry.    Sysco employs over 75,000 associates, has 337 smart distribution facilities worldwide and over 14,000 IoT-enabled trucks serving 730,000 customer locations. For fiscal year 2025 that ended June 29, 2025, the company generated sales of more than $81.4 billion. Sysco LABS Sri Lanka delivers the technology that powers Sysco’s end-to-end operations.       Sysco LABS’ enterprise technology is present in the end-to-end foodservice journey, enabling the sourcing of food products, merchandising, storage and warehouse operations, order placement and pricing algorithms, the delivery of food and supplies to Sysco’s global network and the in-restaurant dining experience of the end-customer.  For more information visit: www.syscolabs.lk    The Opportunity    We are currently on the lookout for a Software Architect - MLOps to join our team. This is a senior leader who will be responsible for building, scaling, and governing the enterprise’s Machine Learning Operations (MLOps) capability - ensuring that all ML and AI models are developed, deployed, and managed in a secure, compliant, and efficient manner.     This role defines the MLOps platform, tools, processes, and operational controls that support end-to-end model lifecycle management, from development through deployment, monitoring, and retraining.     The role partners closely with Data Platform Engineering, which leads CI/CD automation and model lifecycle governance, and leads the Communities of Practice (COPs) for Data Scientists and MLOps Engineers to ensure standardization, scalability, and innovation across the enterprise.         Responsibilities:    MLOps Platform & Tools:  Leading the strategy, design, and operation of the enterprise MLOps platform to support scalable ML/AI model development and deployment  Owning the evaluation, integration, and administration of MLOps tools (e.g., Vertex AI, MLflow, Kubeflow, DataRobot, custom frameworks)  Partnering with the Data Platform Engineering team to align platform integrations with enterprise CI/CD pipelines and lifecycle governance frameworks  Implementing a unified model registry, metadata store, and lineage tracking system to ensure auditability and reproducibility of ML assets  Driving automation for model testing, deployment, monitoring, and retraining, minimizing manual intervention      Processes & Controls for ML/AI:   Defining and maintaining standardized MLOps processes for model release, testing, approval, and production promotion   Establishing operational controls and guardrails for model reliability, fairness, explainability, and compliance.   Embedding AI governance policies (ethical AI, bias detection, transparency, and accountability) into MLOps workflows  Developing automated frameworks for model performance monitoring, drift detection, and retraining triggers  Collaborating with Data Management and AI Governance teams to align on data quality, privacy, and regulatory standards        Governance Metrics & Reporting:   Defining and enforcing MLOps operational standards, policies, and compliance frameworks  Developing KPIs and dashboards to measure operational performance (e.g., deployment frequency, model uptime, drift incidents, retraining efficiency)  Reporting platform health, adoption rates, and compliance performance to senior D&A leadership  Ensuring all deployed models are traceable and visible within the enterprise data catalog and governance framework      Leadership & Team Development:   Building and leading a specialized MLOps Engineering team responsible for platform development, reliability, and enablement  Coaching and mentoring Data Scientists and MLOps Engineers on modern operational practices, automation, and performance optimization  Developing career paths, training programs, and certification plans for MLOps professionals  Fostering a product-based delivery culture, emphasizing agility, scalability, and automation      Performance Metrics:  Enabling reductions in model deployment cycle time and manual intervention  Increasing the percentage of models monitored and retrained automatically  Improving model uptime, performance, and governance compliance  Increasing the platform adoption rate across Data Science teams  Contributing to the maturity growth of MLOps and Data Science COPs      Requirements:  A Bachelor’s or Master’s Degree in Computer Science, Data Engineering, or a related field    8+ years of experience in Data Science, Machine Learning Engineering, or DevOps, including 5+ years in a leadership capacity  Proven experience implementing enterprise-grade MLOps platforms on cloud environments (GCP, AWS, or Azure)  A deep understanding of machine learning lifecycle management, CI/CD automation, and AI governance  Must possess a strong background in cloud-native technologies (Docker, Kubernetes, Terraform, Airflow)  Familiarity with data governance, security, and compliance frameworks (e.g., GDPR, HIPAA, Responsible AI)  Demonstrated ability to lead cross-functional technical teams and drive change across large organizations  Excellent communication and stakeholder management skills with both technical and business leaders  Strategic thinker with the ability to translate complex technical concepts into business value  Passion for operational excellence, automation, and continuous learning       Benefits:   US dollar-linked compensation    Performance-based annual bonus    Performance rewards and recognition    Agile Benefits - special allowances for Health, Wellness & Academic purposes    Paid birthday leave   Team engagement allowance    Comprehensive Health & Life Insurance Cover - extendable to parents and in-laws    Overseas travel opportunities and exposure to client environments    Hybrid work arrangement         Sysco LABS is an Equal Opportunity Employer.