Staff Engineer, Embedded AI

• Position: Staff Engineer, Embedded AI
• Team: Embedded AI Engineering
• Level and grade: L5, Senior Manager equivalent on the Embedded AI Engineering band
• Position type: Full time, permanent
• Reports to: Senior Staff Engineer, Embedded AI
• 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 own a major part of a client system end to end: the design, the build, the deployment and what happens once it is live, including any models inside it.

Two seats are open.

One carries the registry, credential and wallet layer on a rural programme and the platform beneath it; the other carries the equivalent scope on a national self-help group platform.

Most of what we build is not a model.

Registries, credential and wallet layers, group financial records, synchronisation for intermittent connectivity, offline-first clients and the migrations that keep them alive are the bulk of the work, and they have to be engineered properly before any model on top of them is worth anything.

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.

We do not staff a specialist for each component.

Everyone here writes production software and everyone here builds models; the levels are separated by the scope a person carries rather than by the technology they work on.

• Core Job Responsibilities
• Design and build your part of the system: services, APIs, data model, migrations, and the synchronisation and offline behaviour that field conditions demand.
• Build the models inside your scope end to end: problem framing, data assembly and labelling, approach, training, evaluation and deployment.
• Own evaluation. Construct the test sets, choose the metrics, find the leakage, and report performance by subgroup as well as in total.
• Say early when a problem does not need a model, and propose what it needs instead.
• Own the data layer for your component: ingestion, validation, reconciliation against government source systems, and the pipelines that keep it current.
• Own production for your component: reliability, monitoring, alerting, incident response, drift and retraining triggers, and the rollback path.

Hold the engineering discipline in your area: tests, code review, continuous integration, release management and backward compatibility.

• Build for personal data properly: access control, encryption, audit trails and retention rules that match what the institution has committed to.
• Make the infrastructure calls inside your scope: scaling, latency, cost and reliability, designed for constrained hosting and limited connectivity.
• Work closely with the institution.

You spend real time with the people who will use what you build and the people who will run it after we leave, because the constraints that matter are rarely written down.

• Package for handover so that a delivery team or a government IT unit can run, extend and retrain what you built.
• Review the work of the engineers below you and hold the standard on system design, model quality, testing and documentation.

Qualifications and Competencies

• Seven or more years in software engineering, including at least three years training and shipping machine learning models to production.
• Strong conventional engineering: service and API design, relational data modelling, migrations, and systems that other systems depend on.

Models you built yourself that went live, and you can talk through the data, the approach, the evaluation and what broke.

• Python at production standard, with pandas, scikit-learn and at least one of Py Torch or Tensor Flow, plus a second language used for backend work.
• You design evaluation properly: test set construction, metric choice, leakage, and performance across subgroups.

SQL to a serious standard and the data engineering needed to make messy institutional data usable.

• Testing, code review, continuous integration and release discipline as habits rather than as things you have heard of.
• One cloud platform you have worked in seriously, including containers, deployment and cost.
• You use AI coding tools and agents fluently and know where they stop being reliable.
• You can explain a technical constraint to a non-technical government audience without losing the substance of it.
• Also useful, though we will not screen on it
• Work on government or large institutional systems.
• Digital public infrastructure: registries, credentials, wallets or data exchange.
• Offline-first or low-connectivity systems.
• Language models, retrieval or forecasting in production.
• Reviewing and mentoring less experienced engineers.
• 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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