Manager, ML Engineering

Smarsh, India - Bengaluru

The role

We are looking for an experienced Engineering Manager to lead and grow the Cortex team. This is a hands-on leadership role: you will manage a talented team of ML and delivery engineers, drive technical delivery of AI service initiatives, and help define the direction of Smarsh's AI platform capability.

You will report directly to a Senior ML Engineering Manager based in the UK and will be the primary engineering leader for the Cortex team on the ground in Bangalore. You will work closely with Smarsh's Applied Machine Learning team — who build and train our in-house models — as well as Product Management, Technical Program Management (TPM), the Fabric platform organisation, and sister Cognition teams: Cognition Logic and Cognition Analytics, all part of the wider Enterprise Conduct organisation.

What you need

What You Bring

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.

Technical Skills

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.

Manager, ML Engineering

The role

Manager, ML Engineering

The role

What you need

What You Bring

Technical Skills

Team Leadership & People Management

Technical Delivery & Model Operations

AI-First Ways of Working

Technical Strategy, Stakeholder Management & Developer Experience

AI Productivity & Tooling

Leadership & Communication

About Smarsh

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Smarsh