Build and operate production software and embedded AI models for public-sector platforms. Responsibilities include designing APIs and data models, preparing messy institutional data, training and evaluating models, deploying and monitoring services, supporting offline-first systems, writing tests, reviewing code, protecting personal data, and documenting solutions. The role also involves mentoring and reviewing junior engineers, handling production issues, and traveling regularly to client sites.
Position: Senior Software Engineer II, Embedded AI
Team: Embedded AI Engineering
Level and grade: L4, Manager equivalent on the Embedded AI Engineering band
Position type: Full time, permanent
Location: India, with regular travel to client sites
Reports to: Staff Engineer, Embedded AI
Travel: 20 to 25 percent
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 build and ship the services a government platform runs on, and you train the models that sit inside them. You take a defined problem, design the piece, get the data into shape, build it, put it into production and keep it working. Three seats are open across two rural digital infrastructure programmes.
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.
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
- Own services and features end to end: design, data model, build, test, deploy and monitor.
- Write the APIs and integrations that connect what we build to the systems the institution already runs, and keep them backward compatible once other people depend on them.
- Train and ship models for the problems you are given: feature engineering, model selection, training, evaluation, packaging and serving.
- Evaluate honestly. Build the test set before the model, choose metrics that match the decision the model supports, check for leakage, and report performance by subgroup.
- Get institutional data into a usable state. Public-sector data arrives incomplete, late and inconsistent, and ingestion, validation and reconciliation are a real part of the work.
- Build for the field. Intermittent connectivity, older devices, and users who will not retry a failed action.
- Watch what you shipped. Errors, latency, drift, override rates, and users quietly abandoning the tool.
- Write tests that catch real regressions, review other people's code, and keep the build green.
- Handle personal data correctly: access control, encryption in transit and at rest, and audit trails.
- Write documentation so that someone who has never met you can run, extend and retrain what you built.
- Review the work of the engineers below you.
Qualifications and Competencies
- Four or more years building and shipping production software, including models you have trained, evaluated and put into production yourself.
- Solid backend engineering: you have designed APIs and data models, written migrations, and supported what you shipped.
- Python at production standard, with pandas and scikit-learn, and working familiarity with at least one deep learning framework.
- You understand what you are doing statistically: train, validation and test, overfitting, leakage, class imbalance, and why accuracy is often the wrong metric.
- SQL and practical data engineering. You can make messy source data usable without being told how.
- Testing, code review, version control and continuous integration done properly, and the ability to debug a production problem you did not create.
- Comfort with at least one cloud platform and containers.
- You use AI coding tools and agents fluently and know where they stop being reliable.
- You raise problems early rather than working around them quietly.
Also useful, though we will not screen on it
- Digital public infrastructure: registries, credentials, wallets or data exchange.
- Offline-first or low-connectivity systems.
- Language models, retrieval or forecasting in production.
- Work on government or large institutional systems.
- Open-source contributions.
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.
APLYD
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