Data and AI Delivery Leader

Workplace: Remote (India), periodic onsite for kickoffs
About the role
Networth Corp builds data and AI systems for enterprise clients. Real production systems, not slideware.
We're a young firm, scaling deliberately, with a small team that punches well above its weight. You'll work alongside people who genuinely know their craft: data engineers who understand what fails in production, ML engineers who care about evaluation, ontology architects who've built at scale. You'll learn faster here in a year than you will in three at a Big-4 practice.
This role owns delivery health across data and AI engagements. You'll partner with a Senior Engagement Architect as a two-in-a-box on each account. One owns the engagement contract and commercial health. You own execution and delivery health. Your partner will bring you into steering committees, client difficult conversations, and commercial negotiations until you can run them yourself. We coach.
You'll also stay close to the actual work. Close enough to open a notebook when a pipeline is behind, walk a Spark job log, spot a DAX measure that's silently wrong, look at an LLM eval report and know if it's telling you the truth. Not to do the engineers' jobs. To know when something's off before they tell you.
Why this is a great next move
You'll ship things that matter. Our clients are Fortune 500 chemicals, life sciences, financial services, and manufacturing firms. What you deliver runs in their actual operations
You'll own real scope from day one. Small firm, high trust. No waiting three years for a promotion to touch a real client
You'll be around people who care about craft. Design system enforced. Standards enforced. Retros that actually change how we work
You'll be coached, not micromanaged. Two-in-a-box partners bring you into hard conversations early so you learn by being in the room
You'll grow into a Senior Delivery Leader role. With mentorship, portfolio experience, and delivery contributions on your record
What you'll do
Own delivery quality across one or more data and AI engagements as capacity builds
Run the delivery cadence: sprint planning, standups, reviews, retros, milestone tracking, dependency management, escalation
Stay close to the work: read pipeline logs, open the notebook, look at the model card, review the eval report, poke at the dashboard yourself. Show up to squad standups where it matters
Enforce Networth Corp's delivery process. Quality gates (DoR, DoD, data quality gates, model release readiness), artifacts that get used (RAID logs, decision logs, ADRs, data contracts), governance rhythms
Enforce design standards on every client-facing artifact. Ugly slides and cluttered dashboards do not leave the building
Contribute to the knowledge management loop: retros, playbook updates, template refinements, prompt libraries, evaluation harnesses, ontology patterns
Partner with the Senior Engagement Architect on client health, escalations, and steering committee prep. Grow into leading these yourself
Coordinate fractional leads and IC teams across data engineering, ML, LLM/GenAI, ontology, BI, integration, and platform workstreams
Show up in client working groups and squad syncs. Learn to hold steering committee conversations with CIO, CDO, and CTO stakeholders. Your Engagement Architect partner brings you into these until you're ready to run them
Watch for risk proactively: model risk, data quality risk, dependency risk. Flag drift the week it starts
Track commercial hygiene: burn versus forecast, utilisation, invoice cleanness, change requests before scope creeps
How we work
Process is a product. We treat our delivery methodology like software we ship. Versioned, tested, improved every release
Design is not decoration. Every deliverable follows a house design system
Knowledge compounds. Every engagement ends with a documented handover, and every playbook cites the engagement that hardened it
Culture over process, when they conflict. We hold each other to high standards and to kindness in the same sentence. Direct feedback, no politics, no BS
We coach. You'll pair with senior delivery people on the harder stuff. Nobody expects you to walk in knowing how to run a bad-quarter steering committee. We expect you to want to learn
What you'll work with
Client stacks: Databricks · Snowflake · Microsoft Fabric · Azure ML · Bedrock · Azure OpenAI · SAP · Salesforce · dbt · Airflow · Kafka
Data and AI delivery patterns: data contracts · data product thinking · MLOps release cadence · LLMOps evaluation gates · Unity Catalog governance · lineage-first delivery
Delivery frameworks: Scrum, Kanban. SAFe patterns if you've seen them
Tools you'll use daily: Jira · Confluence · Notion · Miro · Loom · Slack · Teams
Tools you'll open when the team is stuck: Databricks notebooks · dbt Cloud · Airflow UI · MLflow · LangSmith or Langfuse for LLM traces · Power BI Desktop · DAX Studio
What we're looking for
2 to 6 years in delivery leadership, project management, tech lead, or an engineering role you're moving out of into delivery
You've shipped data or AI to production. Even if you were part of the team, not leading it. You know what it takes because you've done it
You still open the tools yourself: read a Spark plan, walk a notebook, look at a dbt run, spot a bad DAX measure, review an LLM eval report. You don't have to write the fix. You have to see the problem
Comfortable in modern data and AI stacks: Databricks, Snowflake, Fabric, Azure, or similar. You've used them, not just heard of them
Comfortable running Scrum or Kanban cadence: standups, planning, retros, tracking
Client-comfortable: you can hold a working group conversation with data product owners and tech leads. You want to grow toward CIO and CDO conversations
Process orientation: you like standards and playbooks. You don't resent them
Design taste: opinions about what a good deck looks like, and you're willing to enforce them
Knowledge management instinct: first thing you do after a milestone is write up what happened, and put it somewhere findable
Comfort with the ambiguity of a scaling firm. This is not a Big-4 role with a 30-page methodology to hide behind
Bonus
Started as a data engineer, ML engineer, analytics engineer, or BI developer before moving into delivery. Huge plus
Contributed to a GenAI or agent platform in production
Delivery experience in regulated industries (chemicals, pharma, financial services, life sciences)
Familiar with modern engagement models (T&M, outcomes-based, staff-plus-IP bundles)
Contributed to a team's playbook, methodology, or design system as a named author
Certifications welcome, never required: Databricks, Azure, PMI-ACP, PSM, SAFe
Skills tags
Data and AI Delivery · Delivery Management · Project Management · Databricks Delivery · MLOps Delivery · Agile Delivery · Scrum · Kanban · Data Engineering · Machine Learning · GenAI · Power BI · dbt · Client Relationship Management · Team Coordination · Risk Management · Knowledge Management

Work arrangement
No