The posting
Position: Software Engineer / Senior Software Engineer (3–7 years)
Location: Chennai, India (Hybrid) or Remote
Type: Full-time
Experience: 3–7 years
About Athena Infonomics
Athena Infonomics is a global impact solutions group that applies social science research, data analytics, and technology to advance international development. For over 15 years, we’ve partnered with major global institutions including the Bill & Melinda Gates Foundation, the World Bank, FCDO, US State Department, and leading INGOs to build digital tools, platforms, and data systems that drive evidence-based decision-making. We operate across India, the US, the UK, Kenya, and program hubs spanning Sub-Saharan Africa and South Asia.
We’re expanding our technology practice — building reusable platforms, integrating AI into our solution delivery, and growing the engineering team that makes it all happen.
The Role
You are an engineer with 3–7 years of experience who brings not just technical depth but genuine ownership, vision, and the drive to shape how things are built. You will work closely with the Engineering Manager / Tech Lead on active client engagements — designing and delivering modules end-to-end, integrating AI-powered capabilities, and contributing to technical decisions that matter.
This is not a ticket-and-close role. You are expected to independently own full modules from architecture to deployment, proactively identify problems before they escalate, bring AI fluency to every engagement, and show the kind of leadership character that grows into a senior or tech lead position. You will be held to a high bar — not someday, but from day one.
What You Will Own
Feature Development & End-to-End Delivery
- Own modules and feature sets independently — from reading the requirement to architecting the solution, writing production code, handling QA, and deploying to a live environment.
- Build and maintain backend services, frontend interfaces, and integrations that are clean, tested, and ready to hand over to a client.
- Translate technical specifications — and sometimes ambiguous client inputs — into clear implementation plans before writing a single line of code.
- Meet sprint commitments without being chased. Flag blockers early, propose solutions, and close the loop.
- Contribute to identifying reusable components and patterns across engagements to reduce delivery time.
AI Integration & Applied Intelligence
- Bring working knowledge of LLM integration patterns — RAG, embeddings, prompt engineering, API orchestration — into real client solutions.
- Implement and iterate on AI-powered features: automated reporting, NLP workflows, intelligent data extraction, and decision-support tools.
- Evaluate AI output quality, design feedback loops, and make pragmatic decisions about build-vs-API for AI capabilities.
- Stay current on the AI/ML landscape. Come in with opinions. Push the team’s thinking on what’s possible.
Code Quality & Engineering Standards
- Write code you’d be proud to have reviewed — with tests, clear abstractions, sensible modularity, and production readiness.
- Review PRs critically and constructively. Set the quality bar, not just meet it.
- Improve team standards: CI/CD pipelines, deployment processes, testing coverage, documentation. Identify gaps and close them.
- Debug cross-stack issues (API, frontend, database, background workers, caching) and document what you find.
Technical Vision & Problem Solving
- Think in systems. When picking up a new feature, ask: how does this fit the architecture? What breaks if this scales? What’s the right abstraction?
- Make build-vs-buy and architecture decisions at the module level. Document them. Stand behind them.
- Contribute meaningfully to technical design discussions — proposals, spec reviews, architecture calls. Your voice should add value, not just volume.
- Identify technical debt and surface it proactively, with a proposed fix — not just a complaint.
Collaboration, Communication & Client Exposure
- Work closely with the Tech Lead on shared codebases and shared standards — as a contributor, not a passenger.
- Participate in client-facing sessions: sprint demos, UAT sessions, technical walkthroughs. Communicate clearly with non-technical stakeholders when needed.
- Surface ambiguities in requirements before they become build problems. Ask the right questions early.
- Contribute to internal documentation — technical specs, runbooks, onboarding guides — with the discipline of someone who knows others will depend on it.
Leadership Character & Growth
- Ownership mindset: you see a problem, you fix it or get it fixed. You don’t wait to be told. You don’t point at someone else.
- Dependability: your team should be able to assign you a module and trust it will be delivered — on time, to spec, production-quality.
- Commitment: you follow through. If something isn’t working, you say so early — you don’t go quiet and miss the milestone.
- Vision: you think beyond the ticket. You have opinions about what the product or platform should become and you’re not shy about sharing them.
- Low ego: you’ll pair-program with a more junior colleague in the morning and present a technical approach to a client in the afternoon. The work matters more than the title.
What We’re Looking For
Must Have
Experience & Track Record
- 3–7 years of professional software engineering experience, with a clear track record of shipping.
- Delivered features or modules end-to-end in a professional setting — product company, agency, or consulting environment.
- Hands-on experience with a modern web stack:Backend: Python (Django or FastAPI) with PostgreSQL, Redis, task queues Frontend: React or Angular with TypeScript, component-based architecture Infrastructure: AWS or Azure, Docker, CI/CD pipelines, Nginx or reverse proxies
- Comfortable working on existing codebases — reading, extending, improving code you didn’t write.
- Experience managing your own delivery: scoping, estimation, flagging risk, meeting commitments.
Engineering Depth
- Strong API design sense — RESTful services, authentication, data exchange, third-party integrations.
- Database proficiency: schema design, query optimisation, data modelling for analytics workloads.
- Understands performance end-to-end: frontend load times, API latency, database query plans, caching strategies.
- Writes tests — unit and integration. Treats testing as part of delivery, not an afterthought.
- Can trace a bug across a full stack and fix it without being walked through every step.
AI/ML Fluency (Applied, Not Research)
- Working knowledge of LLM integration patterns: RAG, embeddings, prompt engineering, API orchestration.
- Has built or integrated AI-powered features into a real product or client solution — not just experimented in a notebook.
- Can evaluate AI output quality, design feedback loops, and make pragmatic build-vs-API decisions.
- Keeps up with the AI/ML landscape. Knows what’s possible, what’s hype, and how to apply it practically.
Leadership Character
- Ownership mindset: you take responsibility for your modules end-to-end — from understanding the requirement to verifying it works in production.
- Dependable: your word is your commitment. You deliver, or you raise the flag early enough that the team can adapt.
- Visionary: you care about what’s being built and why. You have opinions on architecture, product direction, and technical quality — and you express them constructively.
- Strong communicator: you write clearly, give direct status updates, and surface issues without being asked.
- Bias for action: you’d rather ship a well-considered 80% solution and iterate than over-engineer and miss the deadline.
Nice to Have
- Experience in the international development, MERL, or social impact sector.
- Experience building data dashboards, analytics tools, or MERL systems.
- Familiarity with data sovereignty requirements (GDPR, in-country hosting mandates).
- Experience with geospatial data (PostGIS, Leaflet, mapping libraries).
Why Join Athena Infonomics
- Mission that matters. Your code powers tools used by governments, donors, and development organisations tackling real problems — sanitation, health, education, climate — across multiple continents.
- Real ownership, from day one. You’ll be given modules to own, not tickets to close. Your decisions affect real clients. Your delivery shapes the product.
- AI at the frontier. We’re integrating AI into how development data is collected, analysed, and reported. You’ll define this capability alongside senior engineers, not inherit it.
- Strong mentorship and high standards. You’ll work directly with experienced engineering leads who pair with you on hard problems, invest in your growth, and hold you to a high bar.
- Variety and depth. No two engagements are the same. You’ll build dashboards, analytics platforms, data pipelines, and AI-powered workflows across a global client base.
- Global exposure. Work with clients and teams across India, the US, East Africa, and South Asia.
Interview Process
- Intro call — Motivation, background, and culture fit
- Technical screen — System thinking, past projects, architecture sense, and AI/ML knowledge
- Hands-on exercise — Write, debug, and walk through code; demonstrate end-to-end thinking and quality standards
- Leadership & delivery round — Ownership mindset, delivery under ambiguity, communication clarity
- Final round — Alignment, expectations, and compensation discussion
Athena Infonomics is an Equal Opportunities Employer
Athena Infonomics 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 Athena Infonomics
