Manager, ML Engineering

Contribute to the Cortex technical roadmap, working with engineering leadership, Product Management, and TPM to align delivery to business priorities.

Build strong working relationships with the Applied Machine Learning team — acting as a bridge between model development and production AI service deployment.

Partner closely with sister Cognition teams — Cognition Logic and Cognition Analytics — to align on shared platform patterns, APIs, and service contracts within the Enterprise Conduct organisation.

Engage proactively with the Fabric organisation on infrastructure, platform standards, and shared tooling dependencies.

Represent Cortex in cross-team forums, architecture reviews, and planning sessions — advocating for Cortex consumers' developer experience.

Help to drive the AI Service Catalogue vision: discoverable, well-documented, and operationally excellent services that product engineers across Smarsh can consume with confidence.

2+ years of engineering management experience, ideally in an AI/ML, platform, or MLOps context.

Solid track record of delivering production ML or AI services at scale.

Experience working at the interface between applied research or ML teams and production engineering — you understand how to take a model from handoff to a reliable, monitored service.

Experience managing distributed teams across geographies and time zones.

Demonstrated ability to build trusted relationships with Product, TPM, and platform stakeholders — translating business priorities into engineering plans and vice versa.

Experience with COGs analysis and FinOps practices for AI/ML workloads — you understand how to track, attribute, and optimise infrastructure and inference costs, and can make informed build vs. buy decisions on managed services.

Solid release management and planning experience — you can own a release calendar, coordinate cross-team dependencies, manage risk gates, and ensure smooth, well-communicated production deployments.

Hands-on engineering background — you can credibly engage with technical design decisions and code reviews.

Proficiency in Python; familiarity with Kotlin or JVM-based frameworks is a plus.

Experience with cloud-native AI/ML infrastructure: AWS (Bedrock, SageMaker, EKS), Kubernetes, and Kafka.

Solid understanding of audio and NLP model architectures — specifically Parakeet (ASR), NeMo framework, and XLM-R based multilingual models.

Solid MLOps foundations with practical production experience across the full model lifecycle — including experiment tracking, model registry management, gated deployment strategies (shadow mode, canary, blue/green with automated quality gates), drift detection, rollback handling, and SLO-linked promotion criteria.

Understanding of ML model serving patterns, including inference optimisation and managed inference platforms (e.g. Triton Inference Server).

Solid understanding of modern AI/ML architectures and system design patterns — including transformer-based models, agentic workflows, RAG, and multi-agent orchestration — with the ability to engage credibly in technical design discussions and evaluate trade-offs.

Comfort with operational excellence practices: SLOs, observability, incident management, and on-call culture.

Active user of AI-powered coding assistants (Windsurf, Claude Code, or similar).

Genuine conviction that AI tooling meaningfully accelerates engineering teams — and a desire to prove it.

Ability to coach engineers on effective use of AI tools and to separate hype from practical value.

Clear, direct communicator — able to translate complex technical topics for non-technical stakeholders.

Data-driven approach to engineering decisions and team health.

Comfortable with ambiguity and capable of bringing structure to new problem spaces.

Collaborative leader who builds trust quickly across cultures and time zones.