Software Engineer / Senior Software Engineer

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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).

• 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.

1. Intro call — Motivation, background, and culture fit
2. Technical screen — System thinking, past projects, architecture sense, and AI/ML knowledge
3. Hands-on exercise — Write, debug, and walk through code; demonstrate end-to-end thinking and quality standards
4. Leadership & delivery round — Ownership mindset, delivery under ambiguity, communication clarity
5. Final round — Alignment, expectations, and compensation discussion