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Tiger Analytics is building Intelligent Product Engineering (iPE), the practice that owns end-to-end product engineering for our most complex client programs, anchored in an AI-native software development lifecycle. We are hiring a Technical Program Manager to run one of its most demanding programs.

Tiger Analytics is a global data and AI services firm working with large enterprises across retail, CPG, financial services, healthcare and technology. Intelligent Product Engineering is where that heritage meets full product and application delivery, with AI as the connective thread.

The program is a computer vision and machine learning product delivered into the hands of field users through a production mobile application. The application is built in React Native with a cross-platform bridge into native machine learning modules, and inference runs both on-device and server-side, each with its own constraints. The program spans model development and evaluation, that hybrid inference path, mobile and backend engineering, data pipelines, and rollout across markets where devices, connectivity, lighting and user behaviour all vary.

The technology is genuinely current; the delivery problem is not, and it is unforgiving.

This is not a coordination role. You will own the plan, the requirements, the gates and the truth about status. The people you work with, architects, ML leads, mobile engineers, client product and technology stakeholders, are experienced and opinionated. The job is to hold the whole system in view, convert ambiguity into agreed and testable scope, and defend a plan with evidence, including when that means telling a client or a senior engineer that a date, a design or a team shape will not work.

This role sits on a program where the technology is genuinely novel and the stakes are real: a computer vision product that has to work in the field, on real devices, for users who will abandon it if it does not. The person who runs it well will have both a rare reference story and a direct hand in defining how iPE delivers AI-native products at scale.

Key Responsibilities:

Program Ownership

- Own the end-to-end plan: scope, sequencing, dependencies and critical path across ML, mobile, backend, data and QA workstreams that release on different clocks.
- Run the operating cadence, such as the planning, design and release gates, risk and issue management and maintain a single version of status that both Tiger and the client trust.
- Own release readiness. Define exit criteria in advance and hold them. Nothing ships because the date arrived.
- Forecast honestly: surface slippage the week it becomes visible, with options and a recommendation attached.

Requirements and Scope Control

- Convert ambiguous business intent into written requirements with testable acceptance criteria before build begins, working with client product owners and market stakeholders.
- Run change control that ties every scope addition to an explicit conversation about timeline, cost and team shape, every time, in writing.
- Maintain traceability from business outcome to requirement to test to release, so scope disputes are settled with a document rather than a recollection.
- Distinguish a genuine requirement gap from late discovery of something that was never specified, and be willing to say which it is.





Technical Judgement and Challenge

- Interrogate estimates, designs and team structures proposed by architects, ML leads and SMEs. Understand enough of the CV/ML lifecycle - data collection and annotation cost, training and evaluation, the gap between offline metrics and field accuracy, drift and retraining - to know when an estimate is thin.
- Push back on timelines, sequencing and staffing with evidence rather than opinion, and escalate cleanly when the answer is still no.
- Hold the hybrid inference path to account: parity and versioning between the on-device and server-side models, behaviour when the device is offline or the model is stale, and the latency, battery, binary-size and accuracy trade-offs that decide where a given inference should run.
- Treat engineering standards as delivery gates, not aspirations: branching and merge strategy across markets, CI/CD, automated test coverage on both the JavaScript and native layers, crash reporting, telemetry and observability, and validation of any configuration that can change behaviour in production.
- Drive diagnosis when the field reports that something "does not work" - separating model performance from application defect from device or environment variance - to a named owner and a fix.

Stakeholder, Field and Commercial

- Act as the primary delivery counterpart for client product and technology leadership, and stay credible with the market and field stakeholders who use the product daily.
- Spend time in market, watching the product in real use. This role travels. Device fleets, store or site conditions, connectivity and user workarounds are not knowable from a dashboard, and the plan should change when what you observe contradicts it.
- Manage third-party vendors and client-side teams whose deliverables sit on your critical path, holding them to dates you do not directly control.
- Co-own the commercial picture with the Client Partner: you bring the delivery evidence behind change requests, resourcing and forecast, and you are expected to flag when scope has moved before it becomes a commercial dispute. The Client Partner owns the negotiation; you own the facts it rests on.
- Communicate difficult news early and without varnish, to audiences ranging from engineers to the client executive sponsor.

Team and Practice Contribution

- Shape the team: role mix, onshore and offshore split, and the judgement of when adding people will not make it faster.
- Mentor delivery and engineering leads, and raise the delivery standard around you.
- Feed reusable assets back into iPE, the gates, checklists, estimation baselines, release playbooks, so the next program starts ahead of this one.

Required Qualifications: We are looking for an engineer-turned-program-leader and the following is expected from the profile:

- 1520 years in software engineering and delivery, including at least five years running multi-team programs as the accountable owner rather than a coordinator.




- Production mobile experience. Has shipped and then operated a mobile application used by real users at scale, including the unglamorous parts: store release cycles, device fragmentation, offline behaviour, over-the-air configuration, crash triage.
- Cross-platform depth. React Native in production, and specifically an understanding of where the bridge to native modules becomes the bottleneck, threading, serialisation cost, memory pressure when passing image data, and why a native ML module cannot be reasoned about as if it were JavaScript.
- Technical origin. Has written or owned code at some point in their career, and can read an architecture or design document and identify what is missing rather than what is present.
- Multi-market or multi-geography rollout experience, including localisation, varying device fleets, connectivity constraints, and field enablement.
- Demonstrated willingness to hold a line. Can describe a specific date, scope item or team structure they refused, the evidence they used, and what happened afterwards.
- Modern delivery engineering fluency: CI/CD, test automation strategy, observability and telemetry, release management, and the trade-offs between speed and control.
- Client-facing consulting or services experience preferred, comfortable operating where the client is also, in practice, the boss, and where commercial consequences follow delivery decisions.
- Willing and able to travel for in-market field observation and client sessions.

Good to have

- Applied CV or ML in production. Direct program experience with computer vision or machine learning that reached real users. Comfortable with evaluation metrics and their limits, annotation and data pipeline economics, retraining cadence, and the trade-offs between on-device and server-side inference. Retail, CPG or field-force products; edge and on-device inference; image capture quality problems in uncontrolled environments; regulated or audited delivery environments

Not a fit: ceremony-only Scrum Master or PMO reporting backgrounds, program managers whose technical decisions were always made elsewhere, and candidates whose mobile or ML exposure is limited to having had such a workstream on their status deck.

What We're Looking For

Beyond experience, this program needs a specific temperament:

Technical Backbone:

You hold your own in a design review, not just a status call. You can read an architecture document, an evaluation report or a crash dashboard and know which question to ask next.

Constructive Obstinacy

You say no early, with evidence, and you stay in the room afterwards. A date you know is wrong never leaves your mouth as a commitment.

Evidence Over Assertion

Your status is backed by data, burn-up, defect trend, telemetry, test coverage, not by confidence. You are the same in the steering committee as in the stand-up.

Systems View

You see model, application, backend, device and user as one system, and you know where the seams are when something fails in the field.

Field Empathy

You have watched real users work in bad lighting, on old devices and worse connectivity, and you let that shape the plan rather than the demo.

Calm Under Escalation

When a release slips or a market escalates, you lead with the facts, the options and the recommendation, in that order.

📌 Technical Program Manager Computer Vision, AI/ML & Mobile (Bengaluru)
🏢 Tiger Analytics
📍 Bengaluru