Manager, Machine Learning Engineering

Role Overview: As an experienced Engineering Manager at Smarsh, you will lead and grow the Cortex team, playing a crucial role in managing a team of ML and delivery engineers. Your primary responsibility will be to drive technical delivery of AI service initiatives and help shape the direction of Smarsh's AI platform capability. You will report directly to a Senior ML Engineering Manager and serve as the key engineering leader for the Cortex team in Bangalore. Key Responsibilities: - Lead, mentor, and grow a team of 4 ML Engineers and 1 Delivery Engineer in India and the UK. - Conduct effective 1:1s, performance conversations, and career development planning. - Establish a high-trust, high-performance team culture focused on continuous improvement. - Manage hiring, onboarding, and team capacity planning as Cortex expands. - Own end-to-end delivery of Cortex initiatives, from planning to production release and post-go-live operational support. - Drive the delivery of new capabilities such as Audio Analytics as a Service, In-App Translation, and Intelligent Agent Review. - Collaborate with the Applied ML team to deploy in-house models from research handoff to production-grade deployment. - Develop and enhance Cortex's gated model deployment pipeline, ensuring models progress through quality gates, shadow mode, canary, and full rollout stages. - Implement model evaluation and monitoring frameworks to track quality, performance drift, and SLO compliance in production. - Maintain and enhance Cortex's operational SLOs, reliability posture, and incident response process. - Ensure that engineering practices, code quality, and architectural decisions align with Smarsh engineering standards. - Actively utilize and promote AI productivity tools like Windsurf, Claude Code, and similar tools to increase team velocity and code quality. - Contribute to the Cortex technical roadmap, aligning delivery with business priorities and working closely with engineering leadership, Product Management, and TPM. - Build strong relationships with the Applied Machine Learning team, acting as a liaison between model development and production AI service deployment. - Collaborate with sister Cognition teams to align on platform patterns, APIs, and service contracts within the Enterprise Conduct organization. - Engage with the Fabric organization on infrastructure, platform standards, and shared tooling dependencies. - Represent Cortex in cross-team forums, architecture reviews, and planning sessions, advocating for a seamless developer experience. - Assist in driving the AI Service Catalogue vision, ensuring discoverable, well-documented, and operationally excellent services for product engineers across Smarsh to consume confidently. Qualifications Required: - 2+ years of engineering management experience, preferably in an AI/ML, platform, or MLOps context. - Proven track record of delivering production ML or AI services at scale. - Experience working at the intersection of applied research or ML teams and production engineering. - Ability to manage distributed teams across geographies and time zones. - Demonstrated capability in building trusted relationships with Product, TPM, and platform stakeholders. Role Overview: As an experienced Engineering Manager at Smarsh, you will lead and grow the Cortex team, playing a crucial role in managing a team of ML and delivery engineers. Your primary responsibility will be to drive technical delivery of AI service initiatives and help shape the direction of Smarsh's AI platform capability. You will report directly to a Senior ML Engineering Manager and serve as the key engineering leader for the Cortex team in Bangalore. Key Responsibilities: - Lead, mentor, and grow a team of 4 ML Engineers and 1 Delivery Engineer in India and the UK. - Conduct effective 1:1s, performance conversations, and career development planning. - Establish a high-trust, high-performance team culture focused on continuous improvement. - Manage hiring, onboarding, and team capacity planning as Cortex expands. - Own end-to-end delivery of Cortex initiatives, from planning to production release and post-go-live operational support. - Drive the delivery of new capabilities such as Audio Analytics as a Service, In-App Translation, and Intelligent Agent Review. - Collaborate with the Applied ML team to deploy in-house models from research handoff to production-grade deployment. - Develop and enhance Cortex's gated model deployment pipeline, ensuring models progress through quality gates, shadow mode, canary, and full rollout stages. - Implement model evaluation and monitoring frameworks to track quality, performance drift, and SLO compliance in production. - Maintain and enhance Cortex's operational SLOs, reliability posture, and incident response process. - Ensure that engineering practices, code quality, and architectural decisions align with Smarsh engineering standards. - Actively utilize and promote AI productivity tools