About the Company
Digital Green is a global non-profit organization with nearly two decades of experience helping smallholder farmers build resilient livelihoods. To date, it has reached over 10 million farmers across India, Kenya, Ethiopia, Nigeria, and Brazil. Its flagship product, FarmerChat, is a multilingual AI-powered advisory platform that delivers timely, locally relevant guidance on climate-resilient practices, pest management, input use, and market access. The platform supports over 15 local languages and leverages a combination of deep agricultural extension expertise and modern AI to empower farmers with better decision-making.
Role Overview
We are seeking a Senior Engineering Manager to lead the design, development, and delivery of Digital Green's technology platforms. This is a senior leadership role responsible for engineering execution, technical direction, and team development across backend, frontend, mobile, and quality engineering functions. The successful candidate will ensure the platform scales reliably and cost-effectively to serve millions of farmers in diverse, low-connectivity markets. Collaboration with AI/ML, Product, Design, MEL (Monitoring, Evaluation, and Learning), Data, and external partner teams is essential to deliver integrated solutions that leverage LLMs, data pipelines, analytics systems, SDK integrations, and multimodal interfaces. The role also drives process improvements using an AI-first approach across engineering workflows.
Key Responsibilities
Technical Leadership & System Design
- Set technical direction and guide architecture for scalable, distributed systems across the stack.
- Ensure alignment with AI-driven use cases, data pipelines, analytics workflows, and SDK integrations.
- Provide senior technical direction for integrating LLMs and AI systems, external SDKs and third-party tools, and data/analytics platforms.
- Ensure maintainability, performance, and long-term evolution of existing and legacy systems.
- Make and own build-vs-buy and architectural trade-off decisions at an organizational level.
Process Improvement & AI-First Engineering Practices
- Drive engineering process improvements using an AI-first approach, including development workflows (Jira, Git, code reviews), CI/CD pipelines, and release processes.
- Champion the adoption of AI-assisted tools for development, testing, and debugging across the organization.
- Reduce cycle time and bottlenecks; increase automation and developer productivity organization-wide.
Cross-Functional & External Collaboration
- Partner closely with Product to define requirements and prioritize roadmap.
- Partner with AI/ML teams to integrate and scale AI systems in production.
- Partner with MEL and Data teams to support analytics, impact measurement, and reporting.
- Partner with Design to ensure high-quality, accessible user experience.
- Interface with external partners and vendors on SDK integrations, platform/tool integrations, and technical coordination.
- Represent engineering in senior/leadership forums, ensuring alignment between technical solutions, product goals, and real-world program needs.
Engineering Delivery, Execution & Resource Optimization
- Own end-to-end delivery of engineering initiatives across backend, frontend, mobile, and QA.
- Ensure timely, high-quality delivery aligned with product and organizational goals.
- Plan and optimize team capacity, resource allocation, and workload distribution across multiple pods or workstreams.
- Drive efficient utilization of engineering resources to maximize output and impact.
- Ensure systems are built and operated with cost efficiency in mind (infrastructure, APIs, tooling).
- Define and track product and engineering metrics (performance, usage, reliability) to support data-driven decision-making.
Team Leadership & Capacity Building
- Manage, mentor, and grow a team of backend, frontend, mobile, and QA engineers, including engineering leads/managers where applicable.
- Ensure clear task allocation, prioritization, and accountability across teams.
- Build a culture of ownership, collaboration, quality, and continuous improvement.
- Own hiring, performance management, and career development for the engineering organization.
Quality, Scale & Operational Excellence
- Ensure systems are deployed and maintained with high reliability and performance at scale.
- Own testing strategy, release quality, and system monitoring across the platform.
- Ensure readiness for large-scale deployments across diverse, often low-connectivity environments.
- Drive continuous improvement in system observability, logging, and incident response.
Tech Stack
- Backend: Distributed systems, microservices architecture (likely Node.js, Python, or Java - implied from industry norms).
- Frontend: Web applications (React, Angular, or Vue.js).
- Mobile: Android/iOS (native or cross-platform like Flutter/React Native).
- AI/ML: Large Language Models (LLMs), AI APIs, data pipelines for model evaluation and feedback loops.
- Data & Analytics: Data pipelines, analytics platforms, impact measurement systems.
- Infrastructure: Cloud services (AWS/GCP), CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions), version control (Git).
- Tools: Jira, Agile/Scrum, code review practices.
- Third-party integrations: SDK integrations, external APIs.
- Monitoring: Observability tools (e.g., Prometheus, Grafana), logging systems.
The role emphasizes building AI-first development workflows and leveraging AI-assisted tools across the engineering lifecycle.