Senior Engineering Manager
About the job
- 10+ years of software engineering experience, with 4+ years in engineering management, including managing managers or multiple engineering functions.
- Bachelor's or Master's degree in Computer Science or equivalent.
- Proven experience leading teams that design, build, and operate scalable production systems.
- Strong understanding of backend technologies such as Python, Django, and Java, as well as frontend and mobile systems including Android and iOS application development, and how they integrate.
- Experience with system design and distributed systems at scale.
- Familiarity with data pipelines, analytics systems, and artificial intelligence integrations, including large language models and application programming interfaces.
- Experience integrating external software development kits and third-party platforms.
- Strong grasp of continuous integration and continuous delivery, Git version control, and modern engineering workflows.
- Ability to balance technical decisions against cost, performance, scalability, and product impact.
- Strong debugging and problem-solving ability for complex system-level issues.
- Strong experience with Agile/Scrum methodologies and driving cross-team coordination and execution.
- Strong leadership and organizational team-management skills, with experience developing other managers or senior engineers.
- High level of ownership and accountability, with comfort operating in ambiguous, real-world environments.
- Strong communication and stakeholder-management skills, both internal and external.
- Set technical direction and guide architecture for scalable, distributed systems across the stack.
- Ensure alignment with artificial-intelligence-driven use cases, data pipelines, analytics workflows, and software development kit integrations.
- Provide senior technical direction for integrating large language models and artificial intelligence systems, external software development kits and third-party tools, and data and analytics platforms.
- Ensure the maintainability, performance, and long-term evolution of existing and legacy systems.
- Make and own build-versus-buy and architectural trade-off decisions at an organizational level.
- Drive engineering process improvements using an AI-first approach, including Jira, Git, code reviews, continuous integration and continuous delivery pipelines, and release processes.
- Champion the adoption of AI-assisted tools for development, testing, and debugging across the organization.
- Reduce cycle time and bottlenecks, and increase automation and developer productivity organization-wide.
- Partner with Product to define requirements and prioritize the 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 a high-quality, accessible user experience.
- Interface with external partners and vendors on software development kit integrations, platform and tool integrations, and technical coordination.
- Represent engineering in senior and leadership forums, ensuring alignment between technical solutions, product goals, and real-world program needs.
- Own end-to-end delivery of engineering initiatives across backend, frontend, mobile, and quality assurance.
- 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, including infrastructure, application programming interfaces, and tooling.
- Define and track product and engineering metrics for performance, usage, and reliability to support data-driven decision-making.
- Manage, mentor, and grow a team of backend, frontend, mobile, and quality assurance engineers, including engineering leads and 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.
- 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.
- Build a scalable, reliable engineering organization and technical roadmap supporting FarmerChat's growth across new markets and languages.
- Build robust integrations between large language model and artificial intelligence systems, data pipelines, and the core product platform.
- Build engineering processes and AI-first workflows that measurably improve delivery speed and quality organization-wide.
- Build a strong bench of engineering leads and managers through mentorship and structured career development.
- Experience building or scaling engineering teams at a mission-driven organization or in emerging markets.
- Experience with systems supporting model evaluation, feedback loops, or product and impact metrics.
- Exposure to low-connectivity, multilingual, or low-literacy user contexts.
- Experience partnering directly with MEL or impact measurement functions.
About the company
Digital Green organizes and supports local farming networks and agronomy resources to give practical, actionable farming advice to smallholder farmers in their own languages. The product works by connecting farmers with local agronomists and extension workers, collecting field data and context, and using AI to translate and tailor recommendations to each farmer’s crop, season, and situation. Advice is delivered through accessible channels and in the farmer’s language, enabling real-time or timely guidance. The company stands out by tying together community networks, on-the-ground agronomy expertise, and AI-driven personalization, rather than relying on a single channel or generic content. The goal is to boost farm productivity and livelihoods by making expert guidance easy to understand and use for farmers with limited resources.
Company Size
51-200
Company Stage
Grant
Total Funding
$1.3M
Headquarters
San Francisco, California
Founded
2008
Get referred to Digital Green
See people who can refer or advise you
Simplify's Take
What believers are saying
- Gates Foundation committed $1.95 million in August 2026 for agriculture and financial inclusion.
- June 2026 usage reached 8M answers, and 70% of active users applied advice.
- Brand refresh and new website in June 2026 signal expanded fundraising and hiring momentum.
What critics are saying
- OpenAI integration risk: Microsoft and OpenAI can raise prices or change APIs in 2027.
- Grant dependence is acute; $1.95 million covers 35 months, not durable self-funding.
- Local ministries can copy validated content and bypass Digital Green by 2028.
What makes Digital Green unique
- Since 2008, Digital Green built farmer-grounded video libraries and localized advisory workflows.
- June 2026 FarmerChat serves 1.6M farmers across six countries in 16 languages.
- Microsoft and OpenAI partnerships give Digital Green multimodal AI, Azure, and distribution.
Help us improve and share your feedback! Did you find this helpful?
Benefits
Remote Work Options
Growth & Insights and Company News
6 month growth
-4%1 year growth
-1%2 year growth
0%Microsoft is teaching AI to speak Dholuo and Kikuyu, starting with Kenyan farmers. Generative AI has a language problem. While models like GPT-4 are proficient in English, their performance drops off a cliff when tasked with low-resource languages or cultural nuances specific to the "global majority" - the projected 85 percent of the world's population that lives outside the West. The issue isn't just translation; it's that the underlying training data simply doesn't exist for many communities. Microsoft is attempting to solve this infrastructure gap with a new initiative called Project Gecko. Led by Microsoft Research Africa in Nairobi, alongside teams in India and the US, the project is building Small Language Models (SLMs) designed to run on low-cost devices, specifically tailored for local needs. The project is starting with agriculture in Kenya and India, debuting a new multimodal AI agent that can watch videos, listen to vernacular speech, and answer farmers' questions with verified accuracy. The "hallucination" Problem in farming. The core problem Project Gecko aims to solve is the disconnect between generic AI models and on-the-ground reality. If a smallholder farmer in Nyeri asks a standard LLM about crop diseases in Kikuyu, the model might hallucinate an answer or provide advice relevant to US industrial farming, not local soil contexts. To fix this, Microsoft has partnered with Digital Green, a global development organization that maintains a library of over 10,000 agricultural videos in more than 40 languages. Previously, this data was siloed and hard to search. Project Gecko processes this data using a new system called the MultiModal Critical Thinking Agent (MMCTAgent). Unlike a standard chatbot that just predicts the next word in a sentence, the MMCTAgent analyzes speech, images, and video content simultaneously. When a farmer asks a question, the agent doesn't just generate text; it reasons across the available media and retrieves the specific video timestamp where the solution is demonstrated. For example, a farmer can ask a verbal question in Kikuyu. The system processes the audio, finds the relevant visual answer in Digital Green's database, and responds with text, audio, and a video clip. Microsoft says field studies in Kenya and India showed this method significantly improved user trust compared to generic AI, largely because the AI "shows its work" by grounding answers in real video content rather than abstract training data. Small models and local data. The technical backbone of Project Gecko relies on Small Language Models (SLMs) rather than massive, cloud-heavy LLMs. There are two reasons for this. First, internet bandwidth and device capabilities in rural Kenya are limited; SLMs require less computing power. Second, existing large datasets for languages like Kalenjin or Maa are virtually non-existent, making it impossible to train massive models effectively. The Project Gecko team had to build the tools for Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) from scratch. They compiled a dataset of 3,000 hours of crowd-sourced Kenyan speech to train and fine-tune these models. Currently, the system supports Swahili, Kalenjin, Dholuo, Maa, Kikuyu, and Somali. "Agriculture has very specific terms, which may change from language to language, and even district to district," says Tanuja Ganu, Director of Research Engineering at Microsoft India. "All those domain-specific nuances need to be understood." Open source and future plans. Microsoft is positioning this as a foundation for broader application, not just a proprietary tool. The MMCTAgent is available now on Azure AI Foundry Labs, and the code has been open-sourced on GitHub. The company is also creating a public leaderboard to benchmark how well AI models perform in African languages, attempting to create a standard for accuracy that currently doesn't exist. While agriculture is the test bed due to its economic importance in Kenya - where it contributes significantly to GDP - Microsoft plans to replicate this "design pattern" for healthcare and education. The idea is to create a playbook for developers to build domain-specific AI tools that don't rely on Silicon Valley-centric data. The team is currently using insights from 130 farmers to refine features like clarifying questions and peer-to-peer knowledge sharing. "Building AI systems from the ground up shaped by the knowledge, languages, and modalities of the global majority yields more innovative, useful solutions for a great number of people," says Ashley Llorens, VP and Managing Director of the Microsoft Research Accelerator. For now, the focus remains on proving that a farmer in Nyeri can get an answer as accurate as a software engineer in Seattle.
In collaboration with One Acre Fund, Digital Green will develop a groundbreaking AI project, Farmer Chat, aimed at supporting small-scale farmers in underserved areas of Sub-Saharan Africa.
The Food and Agriculture Organization (FAO), Digital Green, and the University of the Philippines Los Baños College of Development Communication (UPLB CDC) have released recently Participatory video in agrifood systems and digital environments: A practitioner's guide.
Under the support of Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH, the International Potato Center (CIP) in collaboration with Digital Green and other partners has embarked on a mission to promote Conservation Agriculture and enhance Climate Resilience.
Digital Green, a global development organisation has joined forces with generative AI startup Gooey.AI to introduce Farmer.CHAT, an innovative solution aimed at addressing the challenges faced by farmers who are at the forefront of climate change and water security issues.