Smarsh is a global leader in digital communications compliance and intelligence. We help the world's most regulated industries financial services, healthcare, government capture, archive, and analyse communications at scale. Our Cognition Engineering organisation is at the heart of this mission, building the AI and ML capabilities that power Smarsh's next generation of products
About 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 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 organization, and sister Cognition teams: Cognition Logic and Cognition Analytics, all part of the wider Enterprise Conduct organisation.This role is AI-first. You will be expected to champion and actively use AI-powered engineering productivity tools including Windsurf and Claude Code and embed these practices into the team's day-to-day ways of working. What You Will Do Team Leadership & People Management
Lead, mentor and grow a team of 4 ML Engineers and 1 Delivery Engineer across India and the UK.
Run effective 1:1s, performance conversations, and career development planning.
Foster a high-trust, high-performance team culture grounded in continuous improvement.
Manage hiring, onboarding, and team capacity planning as Cortex expands.
Technical Delivery & Model Operations
Own end-to-end delivery of Cortex initiatives from planning and scoping to production release and post-go-live operational support.
Drive delivery of new capabilities including Audio Analytics as a Service, In App Translation and Intelligent Agent Review.
Work closely with the Applied ML team to take in-house models from research handoff through to production-grade deployment managing integration, validation, and operational readiness.
Own and evolve Cortex's gated model deployment pipeline: ensuring models progress through automated quality gates, shadow mode, canary, and full rollout stages with clear promotion and rollback criteria.
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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
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.
AI Productivity & Tooling
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.
Leadership & Communication
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.
Why Smarsh?Smarsh hires lifelong learners with a passion for innovating with purpose, humility and humor. Collaboration is at the heart of everything we do. We work closely with the most popular communications platforms and the worlds leading cloud infrastructure platforms. We use the latest in AI/ML technology to help our customers break new ground at scale. We are a global organization that values diversity, and we believe that providing opportunities for everyone to be their authentic self is key to our success. Smarsh leadership, culture, and commitment to developing our people have all garnered Comparably.com Best Places to Work Awards. Come join us and find out what the best work of your career looks like
Who can apply
Only those candidates can apply who
have minimum 2 years of experience
Salary
Probation:
Duration:
Salary during probation: After probation:
Annual CTC: Competitive salary
Number of openings
1 Editor’s note
Information above is Internshala's interpretation and paraphrasing of what we found on the shared link.
