Senior Director, AI Engineering

Senior Director, AI Engineering

Hyderabad, India

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Posted on: 10/5/2026 - Application Deadline: -

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We're looking for a Senior Director, AI Engineering This role is Office Based, Hyderabad Office

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