Build and deploy end-to-end production machine learning systems for public-sector institutions. Responsibilities include model development, data pipelines, APIs, infrastructure, evaluation, fairness, reliability, documentation, and institutional handover. The role is hands-on, requiring weekly coding, rapid prototyping, client collaboration, technical scoping for business development, reusable asset development, and mentorship of engineers and interns.
Position: Associate Director, Embedded AI Engineering
Team: Embedded AI Engineering; a senior seat, not the discipline leader
Level and grade: L6 Associate Director on the Embedded AI Engineering band
Position type: Full time
Location: Remote, with regular time at client sites in India. Overlap with Indian business hours required.
Reports to: Chief Executive Officer
Travel: 25 to 30 percent, mostly to client sites in India
About Us
APLYD helps governments, multilateral institutions, development finance institutions and foundations use AI in public systems. Most public-sector AI stops at the pilot. Our work is getting it into everyday service delivery and keeping it running once we leave.
Athena Infonomics has done this work for years. In 2026 we set it up as its own company. We cover strategy and readiness, field data and last-mile reach, design and build, evaluation and audit, and scale and production.
440+ engagements · 240+ global clients · 85+ specialists · 7 countries · 5 continents
The Role
You are the senior engineer on APLYD’s AI work. You work closely with a client institution’s data, systems and people, and you build and deliver what the institution needs. You bring the machine learning capability the rest of the firm does not have, and you work alongside the Senior Manager, Data & Measurement, who measures what you deliver. Embedded describes the way of working, close to the institution and its decisions; it does not require sitting in the institution’s office or country.
This is a hands-on role. You write code every week, and you continue to do so as the team grows.
Core Job Responsibilities
- Work closely with the institution. You spend real time in the departments we serve, with the people who will use what you build and the people who will run it after we leave. Much of what makes public-sector AI fail is only visible in the office: undocumented workarounds, fields that are always left blank, approvals nobody mentions until you need them.
- Build and deliver end-to-end machine learning systems. You design, train, evaluate, package and deploy production models, and you own the stack around them: data pipelines, feature engineering, serving, APIs, and integration into the product or institutional system they run inside.
- Prototype quickly when the situation calls for it, using coding agents and modern tooling, and be clear with the team about which artefacts are temporary and which will be hardened.
- Support business development. Growth colleagues will put you in front of institutions that have not yet signed. You scope on incomplete information, give a number and a timeline you can stand behind, and sometimes build the prototype that makes a proposal concrete.
- Turn one engagement into a reusable asset. Notice when a component, pipeline or evaluation harness could serve the next clients, and make the case for building it that way.
- Turn institutional and product problems into technical approaches that hold up: model choice, system architecture, data requirements, evaluation strategy, and the trade-offs accepted.
- Make the compute and infrastructure decisions: scaling, latency, cost and reliability, designed for the conditions our clients run in, including constrained hosting, limited connectivity, procurement rules that limit what you can use, and long periods without active monitoring.
- Package for handover. Engagements end with the institution owning what we built, so models and pipelines must be reusable, documented, and maintainable by a delivery team or a government IT unit.
- Set the technical standard: model performance, evaluation, fairness across subgroups, robustness and documentation. Mentor the engineers and interns who join after you, and act as the reporting line for the Embedded AI Engineering interns.
Qualifications and Competencies
- 10 or more years in software engineering and machine learning, with production machine learning systems you can describe in detail.
- Hands-on across the whole lifecycle: data, modelling, evaluation, packaging, deployment and monitoring.
- Your recent work includes systems you built yourself, not only ones you directed.
- You build the application and API layer around models, not only the models.
- Working knowledge of compute and infrastructure: cloud platforms, GPUs, scaling, cost control, and deployment into environments you do not fully control.
- You take an ambiguous real-world problem and return a concrete technical approach, including what you would not attempt and why.
- You use AI coding tools and agents fluently and know where they stop being reliable.
- You understand the commercial side of delivery: what a project was sold for, what it cost to deliver, and how technical choices affected that.
- You have built something on a short deadline that changed a commercial outcome, such as a demo that won a contract or a prototype that unblocked a decision.
Also useful, though we will not screen on it
- AI systems built for government, public-sector or large institutional environments.
- Digital public infrastructure, responsible AI practice, or systems designed to be owned by the institution.
- Multilingual, low-resource or last-mile data.
- Mentoring engineers or leading a small technical team.
Additional Requirements
- This position requires successful completion of a reference check and employment verification.
- The successful candidate must not be subject to employment restrictions from a former employer, such as a non-compete, that would prevent performance of the responsibilities described.
- Candidates must declare any current or recent engagement with a government, multilateral or development finance institution that could present a conflict of interest.
APLYD’s Work Culture
At APLYD, we function in an outcomes-based work environment with flexible hours and a high level of autonomy. Professional development and thought leadership are key elements of our business model: we support our team members’ professional growth through on-the-job training, and we encourage the cultivation of our colleagues’ personal brands through participation in panels, events, publications, and other thought-leadership opportunities. We embrace a transparent, open work environment with meaningful leadership pathways for those with inventive ideas and initiatives.
APLYD is an Equal Opportunities Employer
APLYD, part of the Athena Infonomics group, is an equal opportunity employer with a commitment to diversity. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status.
AI Proficiency and Responsible Use
Proficiency in the responsible and sophisticated use of AI is a mandatory requirement for all roles, across all levels and functions at APLYD and Athena Infonomics.
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