Senior Director, AI Engineering

Senior

Director, AI Engineering

• Experience: 15+ years in
• software engineering, including 7+ years leading engineering leaders and teams,
• and 4+ years delivering production AI/ML or Generative AI systems
• Reports to: [VP / SVP, AI

Engineering]

• Scope: 2–3 engineering
• teams (~15–30 engineers) in Hyderabad, led through Engineering Managers and
• Tech Leads, with a mandate to grow
• Shift: General shift, with
• regular evening overlap with US Eastern and Pacific time zones.

About Cornerstone OnDemand

Cornerstone

• OnDemand helps organizations of every size develop, engage, and retain their
• people through a unified talent experience platform. Our software supports more
• than 7,000 customers and tens of millions of learners worldwide. The India
• engineering organization is a core part of Cornerstone’s global product
• engineering, and our AI Engineering organization builds the Workforce AI capabilities and shared platform that power skills intelligence, content
• intelligence, personalization, and agentic experiences across Cornerstone’s
• products.

About the role

• We are hiring a Senior Director, AI Engineering to
• lead Cornerstone’s AI Engineering organization in Hyderabad. You will own the
• people, the technical direction, and the delivery outcomes for multiple AI
• engineering teams building the Workforce AI models, services, and platform that
• make Cornerstone’s products intelligent — skills inference, recommendations,
• content understanding, retrieval-augmented generation, evaluation, and the
• agentic workflows layered on top.
• This is a deeply technical leadership role. You
• will lead through managers and tech leads, but you are expected to be credible
• in the room: shaping AI architecture, pressure-testing retrieval and evaluation
• strategy, questioning inference cost and latency trade-offs, and reading a
• design document or a pull request with real judgment. You will not be writing
• production features day to day, but the quality of your technical instincts
• will set the ceiling for the organization.

You will also

• be one of Cornerstone’s most senior AI voices in India — a genuine peer to AI
• Engineering leadership in the US, accountable for globally distributed
• ownership of real product surfaces rather than delegated execution. Building
• the AI talent bench in Hyderabad, and doing it responsibly with sensitive
• workforce data, is central to the job.
• In this role you will...
• Lead and grow the India AI
• Engineering organization
• Lead 2–3 AI engineering teams (~15–30 engineers) through Engineering Managers and Tech Leads — setting direction, clarifying ownership, and holding a high bar for both delivery and craft.
• Develop the leaders who report to you: coach managers on hiring, performance, feedback, and technical judgment, and build a succession bench so the organization is not dependent on any single person.
• Design the team topology — how AI product, platform, and applied research responsibilities are split so teams have clear, durable ownership and minimal hand-off friction.
• Create an environment where strong engineers do their best work: focused roadmaps, low process overhead, honest retrospectives, and visible career growth.
• Own the technical direction for
• Workforce AI in India
• Set and defend the AI architecture and technical strategy for the systems your teams own — model selection and hosting, retrieval and RAG design, vector and feature stores, agent orchestration, and inference patterns.
• Establish rigorous evaluation and quality practices for AI systems: offline and online eval harnesses, golden datasets, regression gates, human-in-the-loop review, and quality metrics tied to customer outcomes.
• Drive build-vs-buy decisions across foundation models, AWS-native AI services (Bedrock, SageMaker), and third-party tooling — with a clear-eyed view of cost, lock-in, latency, and differentiation.
• Own cloud and cost accountability for AI workloads on AWS: inference cost per request, GPU and token spend, caching and model-routing strategy, rightsizing, and FinOps discipline.
• Lead architecture and design reviews personally, and raise the standard for how the organization writes design documents, reasons about trade-offs, and validates assumptions before building.
• Keep the platform and product layers healthy together — shared services, SDKs, observability, and CI/CD that let AI product teams ship quickly and safely.
• Deliver AI products at scale
• Own end-to-end delivery for your teams’ commitments: multi-quarter roadmap, dependency management, realistic sequencing, and predictable shipping without heroics.
• Partner with Product Management, Design, Data, Security, and SRE to turn ambiguous problem statements into shipped, measurable AI capabilities.
• Move AI work from promising prototype to durable production system — reliability targets, monitoring, graceful degradation, rollback, and on-call ownership.
• Make disciplined trade-offs between customer-visible quality, delivery speed, and long-term platform investment, and explain those trade-offs credibly to executives.
• Partner globally with US teams
• Operate as a peer to AI Engineering, product, and architecture leaders in the US — jointly owning strategy rather than receiving it.
• Negotiate clear charter and ownership boundaries across sites so India-based teams own meaningful, end-to-end product surfaces and are accountable for outcomes.
• Maintain a workable overlap rhythm with counterparts in US Eastern and Pacific time zones, and design async-first practices — written proposals, decision records, recorded demos — so progress does not depend on meeting time.
• Represent India AI Engineering in global planning, architecture councils, and executive reviews, and bring back context that helps your teams make better local decisions.
• Build the talent brand and AI
• bench in Hyderabad
• Own the AI hiring mandate for Hyderabad: workforce planning, sourcing strategy, interview loop design, calibration, and closing senior candidates.
• Raise the technical interview bar for AI roles — practical system design, applied ML and LLM depth, evaluation thinking — and train interviewers to assess it consistently.
• Strengthen Cornerstone’s employer brand as an AI destination in Hyderabad through meetups, conference talks, technical blogs, open-source contributions, and university and intern programs.
• Build internal capability alongside external hiring: upskilling paths for engineers moving into AI work, mentorship structures, and a credible senior technical ladder in India.
• Responsible AI, trust, and
• compliance
• Own responsible AI practice for your organization — bias and fairness testing, explainability, human oversight, model and data documentation, and clear escalation paths.
• Ensure AI systems handling sensitive workforce and HR data meet privacy, residency, and security obligations (GDPR, India’s DPDP Act, customer contractual commitments, SOC 2 / ISO controls).
• Partner with Legal, Security, and Privacy on AI governance, vendor and model risk review, and customer-facing AI transparency commitments.
• Build guardrails into the engineering process rather than bolting them on: data-handling standards, prompt and output safety review, tenant isolation, and auditability by default.

You Have What It

• Takes If You Have...
• 15+ years of software engineering experience, with 7+ years leading engineering teams, including experience managing managers.
• Proven track record of shipping AI/ML or Generative AI systems to production at scale in a product company — not only pilots, POCs, or internal tooling.
• Deep, current technical credibility in AI engineering: LLM application architecture, RAG and retrieval design, embeddings and vector search, fine-tuning versus prompting trade-offs, agent orchestration, and model evaluation.
• Strong, hands-on-derived expertise in AWS — designing and operating cloud-native and AI workloads (EKS/ECS, Lambda, S3, RDS/Aurora, DynamoDB, Bedrock and/or SageMaker).
• Solid engineering foundations in Java and/or Python, distributed systems, API-first design (REST/GraphQL), event-driven architectures, and SQL/NoSQL data modeling.
• Experience with MLOps and production ML operations: CI/CD for models, feature and vector stores, model monitoring and drift detection, observability, and incident response.
• Demonstrated success building and scaling engineering organizations in India, including senior hiring, leveling, performance management, and retention.
• Experience operating effectively in a globally distributed engineering model, partnering with counterparts across US Eastern and Pacific time zones.
• Excellent written and verbal communication — able to write a crisp strategy document, defend a technical position to executives, and explain AI trade-offs to non-technical stakeholders.
• Sound judgment on cost, risk, and responsible AI, with a bias toward measurable customer impact over technical novelty.

Nice to have

• Experience in HR technology, talent management, learning, or skills intelligence domains, or with taxonomy and ontology-driven products.
• Background in multi-tenant enterprise SaaS with strict data isolation, residency, and compliance requirements.
• Exposure to applied research collaboration — working with research scientists, publishing, or productizing novel model work.
• Experience standing up an AI platform or internal developer platform used by multiple product teams.
• Familiarity with AI governance frameworks such as the EU AI Act, NIST AI RMF, or ISO/IEC 42001.
• Prior experience as a site or sub-site engineering leader with budget, vendor, and headcount accountability.
• Advanced degree in Computer Science, Machine Learning, or a related quantitative field.
• What success looks like..
• First 90 days
• You know the systems, the people, and the customer problems well enough to have opinions — and you have earned the trust of your managers, your peers in the US, and your product partners.
• You have an honest written assessment of the organization: team topology, technical debt, AI quality gaps, delivery risks, and hiring needs.
• First 6 months
• Delivery is predictable and teams have clear, durable ownership. Roadmap commitments are met without escalation as the default mechanism.
• An AI evaluation and quality framework is in place, and system quality is tracked against customer-facing metrics.
• Key open roles are filled with strong hires, and the interview loop is calibrated and consistent.
• First 12 months
• India-based teams own significant AI product surfaces end-to-end, and global partners route strategic work here by preference.
• Inference cost, latency, and reliability are measured, understood, and trending in the right direction.
• Responsible AI practices are embedded in the engineering process, not maintained as a separate checklist.
• Cornerstone is a recognized AI engineering destination in Hyderabad, with a strong senior bench and internal leaders ready for more scope.

Why join us

• Real AI impact. Workforce AI ships to more than 7,000 customers and tens of millions of learners — your teams’ work shapes how people find skills, learning, and opportunity at work.
• Genuine ownership. India is a first-class engineering site, not a delivery center. You will own strategy alongside global peers.
• Technical leadership, not just management. This role rewards deep technical judgment and gives you the scope to apply it.
• Modern AI stack. LLMs and Generative AI on AWS (Bedrock, SageMaker), Java and Python services, EKS, vector and
• streaming data infrastructure, and a serious evaluation culture.
• Build something lasting. A clear mandate to grow the AI organization and the talent brand in Hyderabad.
• Core values. Be part of a company that lives by Shattering Boundaries, Sparking Greatness, and Sharing Success.
• #LI-Onsite