AI Centre of Excellence Lead - India

AI Centre of Excellence Lead, India

Associate Director

Interpath | Gurugram

About Interpath

Interpath is a financial advisory business with a broad range of specialisms that was formed to allow our people to do what they do best in an agile, independent and conflict-free setting.

We offer the full spectrum of restructuring services from business performance improvement and value creation through to stress, distress and insolvency offerings. Our advisory practice consists of corporate finance, transaction services, valuations, debt advisory, tax, pensions, forensic accounting and e-discovery services.

We help our clients create, defend, preserve, sustain and grow value through the cycle. We work with businesses and their stakeholders across all areas of the market, from regional SMEs all the way up to large, complex multi-national organisations.

Why join Interpath now?

Interpath is an independent, conflict-free advisory business backed by Bridgepoint, one of Europe's leading private equity investors. AI is a funded part of the firm's growth plan and has executive sponsorship behind it. The India Talent Hub builds and runs the firm's technology, and the AI Centre of Excellence is being established there to build and support AI for the whole firm. A team of five is being recruited now and will be in place at about the same time as this appointment, so delivery starts immediately. This is a build role in a small team. The person who takes it will be close to the work, not managing at a distance from it.

About the role

An Associate Director appointment reporting to the Head of AI, leading Interpath's AI Centre of Excellence within the India Talent Hub in Gurgaon. The Centre of Excellence builds AI solutions for the whole firm, takes them into production and supports them in use. Its scope covers the firm's client-facing service lines and the firm's own corporate functions, and the strongest of the internal solutions are expected to become propositions the firm can take to clients. This is a professional services business, not a product company or an industrial one, and what gets built has to fit how advisory work is actually done.

The Head of AI is a Director-level role based in Europe, reporting to the CTO and AI Steering Committee. The role leads the AI Centre of Excellence and manages a team of five: two AI & Tech Engineering Managers, one PMO Lead and two AI Support colleagues. The team is currently being recruited, with the initial focus on establishing and developing the function.

The role is hands-on as well as strategic. In the early stages, the postholder will personally prototype and build AI solutions as the team establishes its capabilities and moves towards production. As the team matures, the focus will increasingly shift towards technical direction, architecture and design reviews, code reviews and team leadership, while retaining the ability to step into delivery when required.

The role requires the ability to operate effectively at two levels: providing detailed technical leadership to the engineering team while communicating clearly and concisely with partners, service-line leaders and the AI Steering Committee, with a focus on business outcomes, benefits and priorities.

The postholder will be expected to:

• Identify and proactively propose high-value AI use cases, rather than simply respond to requests.
• Establish a structured approach to determine where AI can genuinely add value.
• Assess technical feasibility, risks and potential benefits before work begins.
• Translate complex AI concepts into clear business outcomes for senior stakeholders.
• Build credibility with business partners and challenge assumptions constructively.
• Offer practical alternatives and solutions where they disagree with an approach.
• Build, lead and develop the AI CoE team as the capability scales.

In essence, the role requires a leader who combines strong hands-on AI/technical capability with commercial thinking, stakeholder confidence and the ability to build a new AI capability from the ground up.

Level, Reporting & Decision Rights:

• Associate Director grade, reporting to the Head of AI, with a dotted line to the Managing Director, India Talent Hub.
• Line manages the AI Centre of Excellence: two AI and Tech Engineer Managers, one PMO Lead and two AI Support. The five are recruited ahead of this appointment and join at about the same time. The postholder takes them on and owns the team from day one.
• Holds full people management accountability for the team: objectives and goal setting, performance assessment and calibration, feedback, development, progression, capability, capacity and retention, under the firm's performance framework and the India Talent Hub HR process.
• Owns all later hiring and the structure of the team as the portfolio grows.
• Accountable to the Head of AI for delivery of the agreed AI portfolio across the firm: scope, quality, timeline and production readiness.
• Holds technical design authority for solutions built by the Centre of Excellence, within the architecture, tooling and vendor decisions set by the Head of AI, and is expected to contribute to the build directly.
• Prepares initiatives for the 5-pillar governance gate (Desirability, Feasibility, Viability, Control, Compliance). The go, pause or stop decision stays with the Head of AI.
• Decides sequencing, resourcing and capacity within the approved portfolio and budget. Escalates any trade-off that changes scope, cost or the expected benefit.
• Accountable for the confidentiality, privacy and residency of client and firm data handled by the team, under GDPR, UK GDPR, DPDP 2023 and POPIA.
• Represents the Centre of Excellence at the AI Taskforce and provides the delivery content for AI Steering Committee and Board material prepared by the Head of AI.

Key Responsibilities

Delivery for the Firm

• Deliver the agreed AI portfolio end to end: scope, prototype, pilot, harden and move into production, with the Head of AI's quick wins workstream taking priority.
• Contribute to the build personally. Prototype, write code and work through technical problems alongside the team in the early months, and remain close enough to the work to review design and code credibly thereafter.
• Design solutions so that any service line or office can use them, and apply one set of standards, components and delivery patterns across all work.
• Own solutions once they are live: reliability, run cost, support, model performance and defect resolution, through the AI Support team.
• Build reusable components so that each new use case takes less time and cost than the last.
• Maintain a single delivery view through the PMO Lead covering milestones, dependencies, RAID and capacity, and feed it into the Head of AI's reporting.

Value, Adoption & Measurement

• Baseline every initiative before it is built: what the task costs the firm today in time, cost or quality, and what the solution has to beat. An initiative that cannot be baselined should be challenged before it is started.
• Measure and report benefit after go-live against that baseline, and track adoption separately from benefit. A solution nobody uses has delivered nothing, however well it performs.
• Run this consistently across the portfolio so that the Head of AI can report one credible value picture to the AI Steering Committee and the Board.
• Own the technology and tooling picture for the Centre of Excellence: the models, platforms, data stores, integration and evaluation tooling appropriate to a document-heavy, confidentiality-constrained advisory firm. Propose it, justify it and keep it current with the Head of AI.

 People Leadership

• Take on the team recruited ahead of the start date, establish how it works and set individual objectives within the first few weeks.
• Own the full people cycle for the team: goal setting aligned to the AI portfolio, mid-year and year-end performance assessment, calibration, regular feedback, development plans, progression cases and retention.
• Manage the two Engineer Managers as delivery owners, accountable for design quality, delivered outcomes and the performance of their own team members.
• Build technical depth in the team, and enough understanding of restructuring, transaction services and forensics for the team to know what it is automating.
• Own hiring, onboarding and team structure as the Centre of Excellence grows, including succession for critical roles.
• Support the firm's AI literacy and adoption programme in India, and represent the Head of AI in India-based forums where asked.

 Use Cases & Business Contribution

• Work from a structured method for finding where AI belongs: map how the work is done now, where time and cost sit, where quality fails and what the operating model would look like afterwards. A catalogue of use cases is an output of that method, not a substitute for it.
• Work with service line teams, capability sponsors and the AI Taskforce across the firm to identify and shape use cases, starting with the manual data work in transaction services and comparable delivery bottlenecks.
• Work with the firm's own corporate functions, including finance, HR, IT, marketing and operations, to automate internal processes, and develop the strongest of those solutions into propositions the firm can take to clients. Internal work is not lower value work: it is where the firm proves what it can sell.
• Put candidate use cases to the Head of AI with a clear view on feasibility, effort and data readiness, including those that should not proceed.
• Build the business case with the sponsor, so that adoption and benefit remain the sponsor's responsibility.
• Report back on live use: what is adopted, where solutions fail, what needs improvement and what should be withdrawn.

 Governance, Risk & Data

• Prepare every initiative for the governance gate and keep the team's entries in the central AI opportunity register current and accurate.
• Operate the controls for client confidential data: need-to-know access, separation between engagement teams, residency, retention, and the data terms attached to models and vendors.
• Raise independence, conflict of interest and cross-border data questions early, working with Legal, Data Governance, IT and InfoSec.
• Apply responsible AI practice in the build: evaluation before release, human review where the output carries risk, traceability, and handling of bias and model error.

 Commercial & Vendor

• Apply the buy-before-build principle set by the Head of AI. Build only where the return or the intellectual property case is proven.
• Manage day-to-day delivery with build partners and vendors used by the team, holding them to scope, cost and quality.
• Track the run cost of what the team builds, including platform and token consumption, against the agreed budget.

Essential

AI Delivery Track Record:

10 to 15 years across data, AI and engineering, including 4 or more years leading technical teams. A record of taking AI, ML or GenAI solutions into production at enterprise scale, with specific examples of what went live and the benefit delivered.

Hands-On Technical Contribution:

Still builds. Able to prototype, write code and work a technical problem through personally, and to review design and code credibly. Candidates whose contribution for several years has been direction and oversight alone are not a fit for this role.

Evidence of Personal Contribution:

Able to describe what they personally did on the work they cite, distinct from what their team or their organisation did. Answers that stay in collective terms when a personal example is asked for will be treated as an absence of evidence.

People Leadership:

Experience running the full people cycle for a technical team: objectives, performance assessment, difficult feedback, development and progression, including managing through managers. Evidence of retaining and developing technical staff, supported by specific examples.

Working Without a Large Support Structure:

Experience delivering in a small team without dedicated support functions to fall back on. Candidates whose experience sits entirely within very large organisations should be able to show where they have worked at this scale.

Forming an Existing Team:

Experience taking on a team selected by others, establishing how it works and getting output from it early.

Technical Authority:

Enough depth in LLM and agent architectures, retrieval, evaluation, integration and MLOps to direct and challenge engineering managers on design decisions.

Identifying AI Opportunities:

Experience working with a business function that does not yet know where AI applies, identifying where it does, and securing sponsor ownership. This includes internal corporate functions, not only client-facing ones.

Professional Services Fluency:

Able to talk credibly about how an advisory or professional services business runs: engagement teams, utilisation, the shape of a deal cycle, partner economics, where margin is won and lost, and what AI can and cannot change about any of it. This is fluency, which can be acquired outside the sector, and is tested at interview.

Structured Approach to Opportunity Identification:

Has a method for working out where AI belongs in a business, applied before any use case is named. Candidates whose approach begins and ends with a list of use cases do not meet this criterion.

Technology Stack Judgement:

Able to describe, and defend, an AI and technology stack suited to a professional services firm: models, hosting, data handling, retrieval, integration with the firm's systems, and evaluation. The answer must fit an advisory business with confidentiality constraints, not a product organisation.

Value Realisation, Adoption and Measurement:

Has baselined, measured and reported the benefit of what they built, and tracked adoption separately from benefit. Able to describe how this was governed across a portfolio, not just on a single project.

Connecting Experience to This Firm:

Able to say what they would do here specifically, and why they are the right person for this role rather than for an AI leadership role in general. A recital of past achievements without that translation does not meet this criterion.

Data and Confidentiality Discipline:

Practical experience handling confidential client data under formal controls. Working knowledge of at least one of GDPR, UK GDPR, DPDP 2023 or POPIA, and the ability to apply the others.

Executive Communication, Presence and Constructive Challenge:

Answers the question that was asked, directly and briefly, and can hold a position under challenge. Credible in front of partners and service line leaders without support. Puts forward a better alternative when they disagree with something. Indirect answers that circle the question will be treated as a failure of this criterion, not as a style difference.

Remote and Multi-Country Delivery:

Experience being accountable for delivery to a manager in another country and time zone, serving stakeholders in several locations.

Desirable

Professional or Financial Services Background:

Direct experience within professional services, financial services, restructuring, transaction services, forensics or comparable advisory environments. Having worked in the sector is a preference and is scored. Being fluent about how it runs is essential and is listed above.

Productising Internal Capability:

Experience taking a capability built for internal use and turning it into something sold or offered externally.

India Capability for an Overseas Business:

Experience establishing a technology or delivery team in India as an integrated part of a business headquartered overseas.

Document and Data Workloads:

Experience with document extraction, review, reconciliation and other high-volume data preparation work.

Regulatory & Risk Awareness:

Familiarity with evolving AI regulation, including the EU AI Act, and the associated security, bias and fairness considerations.

Vendor & Partner Management:

Experience holding SaaS providers and build partners to agreed scope, cost and delivery standards.

Education:

Advanced degree in computer science, machine learning or engineering, or an MBA.

Our Values:

Embrace different: - From cultures to mindsets, we welcome them all. Our global presence means we’re inclusive by nature and we believe that people are equal, but not the same.

Do the right thing: - Our comfort zone is uncomfortable; we always make the right decision not what is simply easy or popular. Always consider how to implement these decisions, people will always remember how you made them feel.

All hands on deck: - We stand shoulder to shoulder with colleagues and clients, be that physically or from afar. Our individual expertise may find them answers, but it is through teamwork that it is implemented.

Passion drives success: - The impossible is always possible, we push the boundaries of what is expected because we’re never satisfied with the status quo. Our clients expect a positive result when they engage with us, and it’s only by delivering this that we win.

At Interpath, our people lie at the heart of our business. That's why we provide employees with a competitive and comprehensive reward package including compelling salaries and a range of core and optional benefits. Read more about our benefits; Company Benefits - Interpath 

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Please note that Interpath do not accept unsolicited resumes from third-party recruiters.  Any employment agency, person or entity that submits an unsolicited resume does so with the understanding that Interpath will have the right to hire that applicant at its discretion without any fee owed to the submitting employment agency, person or entity.