Build and maintain production services, endpoints, pipelines, and interfaces while training, evaluating, and deploying machine-learning models. Prepare and reconcile government data through ingestion, validation, feature engineering, and testing. Develop reproducible evaluation harnesses, test systems under real-world connectivity and device constraints, participate in code reviews, maintain documentation, and support reliable deployments. The role targets candidates with up to two years of experience or relevant demonstrated academic and project work.
Position: Software Engineer, Embedded AI
Team: Embedded AI Engineering
Level and grade: L1 on the Embedded AI Engineering band
Position type: Full time, permanent
Location: India
Reports to: Senior Software Engineer II, Embedded AI
Travel: Up to 15 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 write code that goes into systems government departments run on, and you train models that sit inside them, with a senior engineer reviewing your work. Expect to spend a good deal of your first year on data, because a system is only as good as what goes into it and government data arrives in poor condition. 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
- Build and maintain the components you are given: services, endpoints, jobs, pipelines and interfaces, to the standard the team sets.
- Train and evaluate models on the problems you are given, and take them through review to deployment.
- Build the evaluation harnesses: the test sets, the metrics, the subgroup breakdowns, and the scripts that make a result reproducible.
- Reproduce a model result or a system behaviour from scratch and report where it differs.
- Get data into a usable state. Ingestion, validation, feature engineering, and reconciliation against government source systems.
- Write tests and keep them passing, take code review seriously, and keep the build green.
- Test the way the system will actually be used, including on slow connections and older devices.
- Keep documentation current: what was built, how to run it, how to retrain it, and what to check when it fails.
- Ask early when something is not going to work.
Qualifications and Competencies
- Zero to two years of experience, or a degree in computer science, engineering, statistics or a related field with work you can show.
- You write clean, tested code in Python, and you have built something end to end that someone other than you has used.
- You have trained and evaluated a model on real data, end to end, and you can explain the choices you made.
- Working knowledge of pandas and scikit-learn. Familiarity with PyTorch or TensorFlow is expected rather than optional.
- You understand train, validation and test, overfitting, leakage, and why accuracy is often the wrong metric.
- SQL, Git and a shell, and a basic grasp of how a web service and a database fit together.
- You use AI coding tools and have a sense of where they stop being reliable.
- You write clearly and ask for help when you need it.
Also useful, though we will not screen on it
- A cloud platform or containers.
- A project using a public-sector or development dataset.
- Language models, retrieval or forecasting.
- 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.
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