Full Stack Engineer III/IV at IDinsight

About the Role

• This role sits within IDinsight’s Data Science and Engineering team and is best suited for a hands-on, full-stack engineer who enjoys operating with a high degree of autonomy. You will work closely with data scientists, data engineers, domain experts, and partners to translate analytical and AI capabilities into impactful software products.
• Successful candidates are comfortable working through ambiguity, making pragmatic technical decisions, and owning the full lifecycle of features- from early problem framing and design to deployment and ongoing improvement in live environments. The work spans frontend, backend, and data integrations, with a strong emphasis on building tools that are intuitive, reliable, and loved by users.

As a full-stack engineer, the day-to-day work may include:

• Understand workflows, decision points, and constraints, and translate them into clear technical requirements to build web products used in social and policy contexts.
• Making thoughtful decisions about application architecture, data flows, integrations, and user-facing behavior to support scalable, production-grade products.
• Design and implement user-facing products such as dashboards, web platforms, and chat-based tools, working across frontend interfaces, backend services, and data integrations.
• Designing and implementing automated data ingestion pipelines that pull from databases, APIs, and external sources, and transform them into reliable, application-ready inputs.
• Integrate analytics or generative AI components into applications to support workflows such as information retrieval, agent-based decision-making, and automations.
• Ship tools to production, monitor usage and performance, debug issues, and refine both frontend and backend based on real-world feedback.
• Work closely with other engineers, product owners, and domain experts to review code, plan releases, and deliver features end-to-end.
• Write blog posts or present on lessons learned.
• Support teammates through formal and informal coaching and collaboration that enables continuous learning and improvement for the team.

Required Technical Qualifications

• Experience of at least 5+ years in software engineering especially web development, with proficiency in Python for building production systems
• Demonstrated ability to test, deploy, and operate user-facing products (such as dashboards, chatbots, or web applications) in production environments
• Strong background in designing and implementing backend services, including APIs, business logic, and data persistence using frameworks like NextJS or FastAPI
• Expertise in modern frontend frameworks (e.g., React or similar)
• Familiarity with cloud infrastructure and deployment workflows (e.g., AWS or GCP, Docker, CI/CD pipelines)
• Experience integrating external systems and APIs, especially foundational Large Language Models, into applications 
• Ability to work directly with clients to scope problem statements, define solution requirements, and deliver end-to-end solutions
• Clear written and verbal communication skills for collaborating with technical and non-technical stakeholders internally and externally

Other required qualifications:

• Proven ability to work independently on ambiguous problems that involve both people and technology
• Experience contributing to shared codebases, libraries, or platform components
• Strong oral and written communication skills in English. Fluency in languages spoken in countries where IDinsight works is a plus.
• Deeply passionate about evidence-informed policy, global development and improving lives in disadvantaged populations.
• Ability and interest in sharing knowledge, mentoring others, presenting work and reviewing the work of others.
• Open-minded self-starter who will thrive while tackling new, unusual and unpredictable challenges.

Preferred Qualifications

 The most competitive applicants will also have experience in one or more of the following:

• Experience living and working in developing country settings.
• Demonstrated ability to secure funding for technology-for-good initiatives, including grants, funded pilots, or innovation challenge awards.
• Experience with AI engineering and evaluations.
• Experience applying data science methods, such as optimization, unsupervised learning, LLMs, or working with specialized data types (e.g., GIS software like QGIS, satellite imagery).