Digital Green is a global non-profit that has spent nearly two decades helping smallholder farmers build more prosperous, resilient livelihoods—reaching over 10 million farmers to date. Our flagship product, FarmerChat, is a multilingual AI-powered advisory platform that gives farmers timely, locally relevant guidance—from climate-resilient practices to pest management, input use, and market access—in the language and format that works for them. Today FarmerChat reaches farmers across India, Kenya, Ethiopia, Nigeria, and Brazil, with millions of questions asked and answered in over 15 local languages. We combine deep, on-the-ground agricultural extension experience with modern AI to meet farmers where they are, and help them make better decisions, faster.

Computer Vision · Audio/Speech Processing · Image Pipelines · Rapid Prototyping

The Lead AI Engineer will lead the development of high-reliability computer vision and speech/audio processing systems for Digital Green's farmer-facing platforms. We are seeking a Lead AI Engineer with deep experience in computer vision and audio/ASR pipelines to design and deploy end-to-end production systems at scale. You will build optimized inference pipelines for image and audio models and work with multimodal data (audio and image) to serve farmer-facing advisory tools.

In this role, you will also define and run robust evaluation frameworks, ensure model reliability, and operationalize production-grade vision and audio ML pipelines with monitoring and continuous improvement. You will collaborate closely with product, engineering, and field teams to build systems that perform effectively in low-resource, real-world agricultural contexts.

1. Multilingual Speech & Audio Processing for Agricultural and Livestock Use Cases

• Build and evaluate ASR systems optimized for agricultural domain content and rural speech patterns.
• Work with targeted ASR models such as Whisper, Azure Cognitive Services, Speechmatics, or Wav2Vec-based systems.
• Develop pipelines for multilingual transcription, diarization, noise handling, and accent robustness.
• Validate transcription accuracy with field recordings, user-submitted audio, and noisy environments.

2. Computer Vision Models for Crop & Livestock Use Cases

• Develop and evaluate computer vision models for crop and livestock use cases, including crop disease/pest detection, animal identification, livestock health assessment, body condition, behaviour and activity monitoring.
• Build image and video processing pipelines for real-world farmer-submitted content, including low-light, blur, noise, varying camera angles and other challenging field conditions.
• Work with models such as CLIP, Vision Transformers, YOLO, Mask R-CNN, DINO or similar architectures for detection, classification, segmentation and visual assessment.

3. Security, Safety & Compliance

• Test vision and audio models against adversarial inputs, spoofing, and data leakage risks specific to image and speech pipelines.
• Collaborate with security teams to ensure compliance with data privacy frameworks (GDPR, HIPAA, etc.).
• Implement input validation, content filtering, and anomaly detection for audio and image submissions.

4. Cross-Functional Collaboration & Mentorship

• Translate product requirements into model tasks and evaluation strategies for voice and vision pipelines.
• Mentor junior engineers in ASR and computer vision model evaluation, fine-tuning, and deployment.
• Partner with backend, DevOps, and product teams to deliver production-ready features and prototypes.

• Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, or a related field with a focus on Machine Learning, Computer Vision, or Signal Processing.
• 6+ years of proven experience as an AI/ML engineer, data scientist, or software engineer, with hands-on experience deploying machine learning models.
• Deep expertise in computer vision and speech/audio processing pipelines, from data preprocessing through production deployment.
• Experience with ASR models (Whisper, Azure Speech) and multilingual audio processing.
• Hands-on experience with computer vision for classification, detection, and enhancement tasks.
• Proficiency with Python and standard CV/audio frameworks (PyTorch, OpenCV, torchaudio, Librosa, or similar).
• Strong understanding of evaluation harnesses, benchmarking, and QA tooling for perception and speech models.
• Experience with model quantization, pruning, and distillation for on-device or edge deployment.
• Strong interpersonal and communication skills, with the ability to work effectively in a collaborative team environment and contribute to a culture of innovation and excellence.

Nice to Have

• Prior experience building internal tools for evaluation, testing, or data pipelines.
• Contributions to open-source computer vision or audio/speech processing frameworks.
• Experience deploying models with Triton, TorchServe, TensorRT, or similar.

• Multi-modal systems involving speech recognition, image verification, enhancement, and classification for real-world applications.
• Secure evaluation frameworks that become organizational standards.

• Work at the intersection of cutting-edge AI and real-world social impact, building systems used directly by smallholder farmers.
• Own end-to-end AI systems — from research and prototyping through production deployment at scale.
• Collaborate with a mission-driven, multidisciplinary team spanning engineering, product, and field operations.
• Backed by leading global philanthropic partners, with the resources and runway to build for the long term.

Digital Green is a great place to work and prides itself on a competitive and comprehensive compensation and benefits package. Digital Green Trust is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law. Please send your application along with an updated CV and a cover letter explaining your suitability for the position. All completed applications should be applied through the Digital Green careers page.

Join us at Digital Green and leverage your expertise in AI to transform agricultural practices and improve the livelihoods of smallholder farmers worldwide.