{"id":96636,"url":"https://alion.io/job/smarsh-manager-ml-engineering","title":"Manager, ML Engineering","company":{"id":1738,"name":"Smarsh","domain":"smarsh.com","url":"https://alion.io/company/smarsh","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":91,"open_postings":17,"ghost_share":0,"stale_share":0.176,"repost_share":0,"time_to_fill_p50_days":67,"computed_at":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":12500,"max_usd":32000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":12},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Claude Code","optional":false},{"name":"Cortex","optional":false},{"name":"FinOps","optional":false},{"name":"Kotlin","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"Windsurf","optional":false},{"name":"Agentic Workflows","optional":true},{"name":"Amazon EKS","optional":true},{"name":"Amazon SageMaker","optional":true},{"name":"Apache Kafka","optional":true},{"name":"AWS","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"Incident Management","optional":true},{"name":"Kubernetes","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"NLP","optional":true},{"name":"NVIDIA NeMo","optional":true},{"name":"Prometheus","optional":true},{"name":"RAG","optional":true},{"name":"Speech Recognition","optional":true},{"name":"Triton Inference Server","optional":true}],"status":"live","first_seen_at":"2026-05-19T17:57:23Z","employer_posted_date":"2026-05-19","last_verified_at":"2026-10-16T20:24:38Z","board_verified":true,"closed_at":null,"days_open":150,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":149},"description":"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.\nYou 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.\nThis is a hybrid role based in our Bangalore office, with the expectation of 3 days per week on-site.\nThis 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\nWhat You Will Do\nTeam Leadership & People Management\nLead, mentor and grow a team of 4 ML Engineers and 1 Delivery Engineer across India and the UK.\nRun effective 1:1s, performance conversations, and career development planning.\nFoster a high-trust, high-performance team culture grounded in continuous improvement.\nManage hiring, onboarding, and team capacity planning as Cortex expands.\nTechnical Delivery & Model Operations\nOwn end-to-end delivery of Cortex initiatives - from planning and scoping to production release and post-go-live operational support.\nDrive delivery of new capabilities including Audio Analytics as a Service, In App Translation and Intelligent Agent Review.\nWork 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.\nOwn 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.\nEstablish model evaluation and monitoring frameworks - tracking quality, performance drift, and SLO compliance in production.\nMaintain and improve Cortex's operational SLOs, reliability posture, and incident response process.\nEnsure engineering practices, code quality, and architectural decisions meet Smarsh engineering standards.\nAI-First Ways of Working\nActively use and champion AI productivity tooling: Windsurf, Claude Code, and similar tools.\nSet the standard for how the team leverages AI-assisted development to increase velocity and code quality.\nIdentify and help to introduce new AI tooling where it adds measurable value to the team.\nTechnical Strategy, Stakeholder Management & Developer Experience\nContribute to the Cortex technical roadmap, working with engineering leadership, Product Management, and TPM to align delivery to business priorities.\nBuild strong working relationships with the Applied Machine Learning team - acting as a bridge between model development and production AI service deployment.\nPartner 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.\nEngage proactively with the Fabric organisation on infrastructure, platform standards, and shared tooling dependencies.\nRepresent Cortex in cross-team forums, architecture reviews, and planning sessions - advocating for Cortex consumers' developer experience.\nHelp to drive the AI Service Catalogue vision: discoverable, well-documented, and operationally excellent services that product engineers across Smarsh can consume with confidence.\nWhat You Bring\n2+ years of engineering management experience, ideally in an AI/ML, platform, or MLOps context.\nSolid track record of delivering production ML or AI services at scale.\nExperience 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.\nExperience managing distributed teams across geographies and time zones.\nDemonstrated ability to build trusted relationships with Product, TPM, and platform stakeholders - translating business priorities into engineering plans and vice versa.\nExperience 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.\nSolid 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.\nHands-on engineering background - you can credibly engage with technical design decisions and code reviews.\nTechnical Skills\nProficiency in Python; familiarity with Kotlin or JVM-based frameworks is a plus.\nExperience with cloud-native AI/ML infrastructure: AWS (Bedrock, SageMaker, EKS), Kubernetes, and Kafka.\nSolid understanding of audio and NLP model architectures - specifically Parakeet (ASR), NeMo framework, and XLM-R based multilingual models.\nSolid 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.\nUnderstanding of ML model serving patterns, including inference optimisation and managed inference platforms (e.g. Triton Inference Server).\nSolid 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.\nComfort with operational excellence practices: SLOs, observability, incident management, and on-call culture.\nAI Productivity & Tooling\nActive user of AI-powered coding assistants (Windsurf, Claude Code, or similar).\nGenuine conviction that AI tooling meaningfully accelerates engineering teams - and a desire to prove it.\nAbility to coach engineers on effective use of AI tools and to separate hype from practical value.\nLeadership & Communication\nClear, direct communicator - able to translate complex technical topics for non-technical stakeholders.\nData-driven approach to engineering decisions and team health.\nComfortable with ambiguity and capable of bringing structure to new problem spaces.\nCollaborative leader who builds trust quickly across cultures and time zones.","description_format":"text","description_chars":6858,"description_truncated":false,"requirements":{"experience_years_min":2,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United Kingdom","iso":"GB","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Security Compliance","RegTech, AML & Compliance"],"lifecycle":[{"event":"open","at":"2026-05-19T17:57:23Z"}],"visa":[],"liveness":{"score":11,"band":"cold","label":"Long shot","p_open":1,"p_active":0.395,"p_room":0.28,"age_days":149,"expected_fill_days":67,"reasons":["conf:3","win:tail","crowd:junior"],"computed_at":"2026-10-16T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/smarsh-manager-ml-engineering","json_url":"https://alion.io/job/smarsh-manager-ml-engineering.json","meta":{"generated_at":"2026-10-16T22:02:47Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":3379,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-17T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":1738},"rest":"https://alion.io/mcp/rest/get_company?id=1738"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fsmarsh-manager-ml-engineering"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fsmarsh-manager-ml-engineering"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fsmarsh-manager-ml-engineering"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/smarsh-manager-ml-engineering\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fsmarsh-manager-ml-engineering"}]}