Lead Ml/ai Engineer

Job Title Lead ML AI Engineer Location Bangalore Reports to AI Director About Digital Green is a pioneer global not for profit organization utilizing digital platforms and community-driven approaches to amplify the voices of smallholder farmers and improve their livelihoods Our mission is to create a world where farmers use technology and data to build prosperous communities By harnessing the power of technology we facilitate knowledge sharing capacity building and market linkages enabling farmers to adopt sustainable agricultural practices and increase their productivity and income We are dedicated to transforming the lives of under-served smallholder farmers worldwide through innovative technology solutions Backed by leading philanthropic organizations such as the Bill Melinda Gates Foundation BMGF Walmart Foundation USAID and UK Foreign Commonwealth Development Office UKFCDO we are committed to leveraging data and technology to empower smallholder farmers and strengthen agricultural extension systems Job Summary LLMs Embeddings Agentic Flows Computer Vision Audio Processing Generative AI Rapid Prototyping The Lead ML AI Engineer will lead the development of high-reliability LLM and generative AI systems for Digital Green s farmer-focused platforms We are seeking a Lead ML AI Engineer with deep experience in LLMs generative AI and agentic architectures to design and deploy end-to-end production systems at scale You will build optimized LLM inference pipelines implement RAG and embedding-based workflows and work with multimodal data text audio image to serve farmer-facing advisory tools In this role you will also define and run robust evaluation frameworks ensure model safety and reliability and operationalize production-grade ML pipelines with monitoring and continuous improvement You will collaborate closely with product engineering and field teams to build AI systems that perform effectively in multilingual low-resource and real-world agricultural contexts Key Responsibilities LLM Development Optimization Evaluation Fine-tune adapt and optimize large language models for domain-specific use cases summarization reasoning conversational agents generative tasks Design test and refine prompts using advanced techniques CoT in-context learning few-shot prompting agent orchestration Build internal prompt libraries reusable templates and evaluation datasets Develop robust evaluation frameworks for accuracy coherence hallucination rate safety bias and reasoning ability Automate evaluation pipelines to validate model updates and fine-tuned variants Agentic Workflows Agent Evaluation Architect and implement agent-based systems with tool use memory planning and multi-step task execution e g LangChain MCP-based systems Validate agent behavior through simulation of real-world workflows handling tool calls vector DB retrieval error recovery and escalation logic Stress-test agents in ambiguous or multi-turn scenarios for robustness determinism and reliability Build monitoring tools for agent behavior performance drift and anomalous actions Automatic Speech Recognition ASR for Multilingual Agricultural 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 4 Computer Vision Models for Agricultural Imagery Develop and evaluate image models for crop detection disease pest classification and agri-specific object identification Build image enhancement and preprocessing techniques for real-world farmer-submitted images low light blur noise Work with CLIP Vision Transformers YOLO Mask R-CNN or DINO models for mobile-friendly and production-grade deployments 5 Embeddings Retrieval Knowledge Systems Deep technical understanding of modern embedding models e g Sentence Transformers E5 GTE Nomic Cohere embedding families and their behavior across languages domains and modalities Build and fine-tune agriculture-specific embedding models optimized for farmer speech rural terminology crops pests soils weather and region-specific agricultural practices Create multilingual embedding pipelines for Indian languages e g Hindi Telugu Kannada Bengali Odia handling code-mixing transliteration dialectal variations and noisy field data Evaluate embedding quality using custom relevance metrics clustering tests semantic similarity benchmarks and domain-specific retrieval tasks Design RAG workflows powered by fine-tuned embeddings ensuring improved context recall precision and diversity 6 Security Safety Compliance Test LLMs and agents against vulnerabilities including prompt injection adversarial inputs data leakage and sandbox escape attempts Collaborate with security teams to ensure compliance with data privacy frameworks GDPR HIPAA etc Implement guardrails safety evaluators and content filtering systems 7 Cross-Functional Collaboration Mentorship Translate product requirements into model tasks prompts and evaluation strategies Mentor junior engineers in LLM evaluation fine-tuning embeddings and agentic systems Partner with backend DevOps and product teams to deliver production-ready features and prototypes Qualifications Skills Education Bachelor s Master s or Ph D degree in Computer Science Engineering or a related field with a focus on machine learning and NLP Experience Proven experience of 6 years as a AI ML engineer data scientist or software engineer with hands-on experience in deploying machine learning models Deep expertise with LLMs embeddings RAG workflows and agentic architectures Experience with ASR models Whisper Azure Speech multilingual audio processing Hands-on experience with computer vision for classification detection and enhancement tasks Proficiency with Python Transformers LangChain SentenceTransformers Strong understanding of guardrails evaluation harnesses and safety tooling Experience with RLHF LoRA quantization distillation Prior experience building internal tools for evaluation testing or data pipelines Contributions to open-source LLM frameworks or multimodal systems Experience deploying models with Triton TorchServe TensorRT or similar Team Player 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 What You ll Build Reliable high-accuracy LLM systems powering real-world products Multi-modal systems involving Speech Recognition Image Verification Enhancement and Classification for real world applications Autonomous agents capable of tool use planning and multi-step decision making Scalable RAG pipelines with multimodal search and domain-specific embeddings Secure evaluation frameworks that become organizational standards Compensation and Application Process 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 https digitalgreen applytojob com apply rH3DWsfxyd Lead-MLAI-Engineer Join us at Digital Green and leverage your expertise in AI to transform agricultural practices and improve the livelihoods of smallholder farmers worldwide Powered by JazzHR