Machine Learning and Operations Consultant Job at Alliance of Bioversity International an…

AI summary

The Alliance of Bioversity International and CIAT seeks an MLOps Consultant to develop and deploy AI solutions for the SIKIA platform in agricultural research. Responsibilities include ASR systems, LLM workflows, multimodal AI, and CI/CD pipelines. Requires a Master's in Computer Science or related field.

• Focus on agriculture and AI integration
• Multilingual ASR and LLM deployment
• Work in Arusha, Tanzania
• Full-time consultant role

Description

Full Time Machine Learning and Operations Consultant Job at Alliance of Bioversity International and CIAT Posted 1 month ago by Alliance of Bioversity International and CIAT Arusha Job Description The Alliance of Bioversity International and CIAT is seeking a highly motivated and technically skilled Machine Learning and Operations (MLOps) Consultant to support the development, deployment, and operationalization of advanced artificial intelligence solutions within the SIKIA platform. SIKIA is a voice-first and multimodal AI system designed to support agricultural research, crop improvement programs, and field-based decision-making through innovative technologies including speech recognition, natural language processing, multimodal AI, and agentic workflows. This role offers an exciting opportunity for AI and machine learning professionals to work at the intersection of technology, agriculture, and international research. The successful candidate will help transform machine learning research into scalable real-world applications, supporting digital agriculture initiatives and improving the connection between crop breeding programs and farming communities across diverse environments. Responsibilities: Support deployment and optimization of multilingual Automatic Speech Recognition (ASR) systems for cloud and mobile platforms. Develop workflows for speech data collection, transcription, evaluation, and continuous model improvement. Implement automated model retraining and fine-tuning pipelines using newly collected field data. Support deployment of Large Language Model (LLM) workflows for conversational analysis and trait extraction. Monitor machine learning models for performance, latency, reliability, and data drift in field environments. Optimize AI inference workflows for low-connectivity and resource-constrained environments. Develop multimodal AI pipelines integrating speech, text transcripts, metadata, and field images. Implement workflows for multimodal data validation, annotation, synchronization, storage, and dataset versioning. Support training, evaluation, and deployment of multimodal and visual-language models. Develop scalable systems for managing large multimodal datasets and AI outputs on cloud infrastructure. Support benchmarking, reproducibility, and optimization of AI workflows for field deployment. Contribute to AI-based disease detection and severity scoring systems using field images and multimodal data. Develop data pipelines for disease annotation, validation, benchmarking, and continuous model improvement. Integrate disease detection workflows within SIKIA and ONA platforms for field-based data collection and analysis. Build and maintain CI/CD pipelines for model training, testing, evaluation, and deployment. Manage experiment tracking systems, model registries, and dataset versioning frameworks. Implement monitoring, logging, and observability solutions across machine learning services. Collaborate with software engineers to integrate Retrieval-Augmented Generation (RAG) pipelines into agentic AI architectures. Deploy and manage machine learning services on Google Cloud Platform (GCP), FAIRGrounds, and related infrastructure. Integrate machine learning services with mobile applications, APIs, backend systems, and cloud platforms. Ensure compliance with data governance, privacy, security, and responsible AI requirements. Qualifications: Master’s Degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, or a related field. Strong academic foundation in machine learning, software engineering, and data systems. Additional certifications in cloud computing, machine learning, or DevOps are advantageous. Experience: Minimum of three (3) years of experience in Machine Learning Engineering, MLOps, or AI deployment. Proven experience building, deploying, monitoring, and maintaining machine learning systems. Experience working with machine learning lifecycle management, model versioning, and deployment workflows. Practical experience deploying AI applications in production environments. Experience working with cloud-based machine learning infrastructure. Exposure to agricultural technology, digital agriculture, or international research projects is an advantage. Knowledge and Skills: Strong programming skills in Python. Experience with machine learning frameworks such as PyTorch or TensorFlow. Knowledge of cloud platforms including Google Cloud Platform (GCP), AWS, or Microsoft Azure. Expertise in machine learning operations (MLOps) principles and practices. Strong understanding of Natural Language Processing (NLP), speech recognition technologies, and conversational AI. Experience working with Large Language Models (LLMs) and generative AI systems. Knowledge of multimodal AI, computer vision, and image-processing workflows. Experience implementing CI/CD pipelines and DevOps practices for AI systems. Understanding of model monitoring, observability, logging, and performance optimization. Familiarity with data governance, security, and responsible AI frameworks. Strong analytical and problem-solving abilities. Excellent communication and technical documentation skills. Ability to collaborate effectively with multidisciplinary teams. General Requirements: Passion for applying AI technologies to real-world agricultural and research challenges. Ability to work effectively in cross-functional and multicultural environments. Strong attention to detail and commitment to quality. Ability to manage multiple projects and priorities simultaneously. Commitment to continuous learning and staying current with emerging AI technologies. Strong organizational and project management skills. Ability to work independently while contributing effectively to team objectives. Professionalism, integrity, and adherence to ethical AI practices. This position offers an outstanding opportunity for career advancement in artificial intelligence, machine learning operations, and digital innovation. As a Machine Learning and Operations Consultant, you will gain hands-on experience with cutting-edge technologies including MLOps, Large Language Models, multimodal AI, speech technologies, computer vision, cloud infrastructure, and agentic AI systems. The expertise developed in this role can lead to future career opportunities such as Senior Machine Learning Engineer, MLOps Lead, AI Solutions Architect, Data Science Manager, AI Product Manager, Head of AI Engineering, Research Scientist, or Director of Artificial Intelligence within global technology, research, agricultural, and innovation organizations. 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