Background

The NDIZI (NLP to Develop and Innovate Zero-shot Intelligence) project seeks a dedicated Machine Learning Operations (MLOps) Consultant to support the development, deployment, and operationalization of machine learning systems powering SIKIA.

SIKIA is a voice-first and multimodal AI platform for conversational data collection and analysis. The consultancy will also support related AI-driven research workflows.

The consultant will work across speech, NLP, multimodal AI, and agentic pipelines, helping transition models and data systems from research prototypes into reliable and scalable solutions suitable for real-world field deployment.

These systems are being developed to help crop improvement systems connect formal breeding processes within controlled experimental trials with real-world on-farm environments.

Principal use cases include:

• Identifying farmer preferences.
• Assessing plant disease occurrence and scoring.
• Supporting environmental response, particularly climate adaptation.

Outputs from this consultancy will be used to address these use cases.

About the position

The consultant will support end-to-end MLOps workflows across data ingestion, validation, dataset versioning, model training, evaluation, deployment, monitoring, and continuous improvement.

The work will cover both cloud and edge environments.

The role will involve close collaboration with research machine learning, software engineering, product, and field teams to ensure systems are robust, maintainable, and aligned with project needs.

The consultant will also support the integration of ML systems within the SIKIA platform, including deployment workflows connecting:

• Mobile applications.
• Cloud infrastructure.
• Speech and multimodal pipelines.
• Disease detection and severity scoring workflows.
• Backend services.
• FAIRGrounds-integrated systems.

The role will also contribute to strengthening best practices around:

• Experiment tracking.
• Model governance.
• CI/CD workflows.
• Deployment automation.
• ML system monitoring across NDIZI infrastructure.

This is an 11-month full-time consultancy position, with potential for extension, based at the Alliance office in Arusha, Tanzania.

Key activities and specific terms of reference

Speech and NLP systems refinement

The consultant will:

• Support the deployment and optimization of multilingual ASR pipelines for cloud and mobile environments.
• Develop workflows for speech data ingestion, transcription, evaluation, and continuous model improvement.
• Implement automated retraining and fine-tuning pipelines using newly collected field data.
• Support deployment of LLM-based workflows for conversational analysis and trait extraction.
• Monitor model performance, latency, reliability, and drift under field conditions.
• Optimize inference workflows for low-connectivity and resource-constrained environments.

Multimodal pipeline development and deployment

The consultant will:

• Support development of multimodal pipelines linking speech, transcripts, metadata, and field images collected through SIKIA.
• Implement workflows for multimodal data validation, synchronization, storage, annotation, and dataset versioning.
• Support training, deployment, and evaluation of multimodal and visual-language AI models.
• Develop scalable workflows for managing multimodal datasets and model outputs across cloud infrastructure.
• Support benchmarking, reproducibility, and optimization of multimodal AI pipelines for field deployment.

Disease detection and severity scoring

The consultant will:

• Support development and deployment of AI workflows for disease detection and severity scoring using field images and multimodal data.
• Implement data and evaluation pipelines for disease annotation, validation, benchmarking, and continuous model improvement.
• Support integration of disease scoring workflows within the SIKIA and ONA platforms for field-based data collection and analysis.

MLOps infrastructure and SIKIA integration

The consultant will:

• Develop CI/CD pipelines for model training, evaluation, and deployment.
• Manage experiment tracking, model registries, and dataset versioning workflows.
• Implement monitoring and logging across ML services.
• Collaborate with the software development team to integrate RAG pipelines into an agentic deployment structure.
• Support deployment of ML services on GCP, FAIRGrounds, and related infrastructure.
• Support integration of ML services within the SIKIA platform across mobile, backend, API, and cloud systems.
• Ensure compliance with data governance, security, and responsible AI requirements.

Deliverables and payment schedule

Deliverable 1: Inception report and technical workplan

Timeline: Month 1, approximately Week 4

The consultant will develop an inception report outlining the technical approach, deployment priorities, infrastructure requirements, integration roadmap, and detailed 11-month workplan for MLOps, speech, multimodal, and disease-scoring workflows across the SIKIA platform.

Deliverable 2: Model development, evaluation, and infrastructure setup

Timeline: Month 5, approximately Week 20

The consultant will establish core MLOps infrastructure and workflows, including CI/CD pipelines, experiment tracking, dataset versioning, model registries, and monitoring systems.

The consultant will also support development, evaluation, and optimization of speech, multimodal, and disease-scoring models across cloud and edge environments.

Deliverable 3: SIKIA AI pipeline integration and deployment

Timeline: Month 8, approximately Week 32

The consultant will deliver integrated deployment workflows connecting ML services with SIKIA mobile applications, APIs, backend systems, and FAIRGrounds-integrated infrastructure.

The consultant will provide operational pipelines for speech processing, conversational analysis, multimodal workflows, and disease-scoring services, including deployment documentation and integration support.

Deliverable 4: Monitoring, evaluation, and optimization framework

Timeline: Month 10, approximately Week 40

The consultant will implement monitoring, logging, benchmarking, and evaluation workflows for deployed ML systems, including ASR performance tracking, multimodal pipeline evaluation, disease-scoring validation, and model drift monitoring.

The consultant will also provide recommendations for optimization, scalability, and field deployment improvements.

Deliverable 5: Final technical report and handover package

Timeline: Month 11, end of assignment

The consultant will submit a final technical report summarizing completed workflows, deployed infrastructure, system performance, key learnings, and recommendations for future scaling and maintenance.

The consultant will also deliver finalized documentation, deployment guides, pipeline configurations, and knowledge-transfer materials for internal teams.

Education

Applicants must have a master’s degree in one of the following fields:

• Computer science.
• Data science.
• Artificial intelligence.
• Software engineering.
• A related field.

Experience

Applicants must have at least 3 years of experience in one or more of the following areas:

• Machine learning engineering.
• MLOps.
• Deployment of AI systems.

Technical competencies

Applicants should have:

• Experience building and managing ML workflows, including model training, deployment, monitoring, and versioning.
• Strong programming skills in Python.
• Familiarity with ML frameworks such as PyTorch or TensorFlow.
• Experience working with cloud platforms such as GCP, AWS, or Azure.
• Strong experience with NLP, speech technologies, conversational AI, or LLM-based applications.
• Experience with multimodal AI, computer vision, or image-based AI workflows. This is desirable.
• Ability to work collaboratively across technical, product, and field teams.
• Strong communication, documentation, and problem-solving skills.
• Background or experience in agriculture, digital agriculture, or international research environments. This will be an added advantage.

• Support the deployment and optimization of multilingual ASR pipelines for cloud and mobile environments.
• Develop workflows for speech data ingestion, transcription, evaluation, and continuous model improvement.
• Implement automated retraining and fine-tuning pipelines using newly collected field data.
• Support deployment of LLM-based workflows for conversational analysis and trait extraction.
• Monitor model performance, latency, reliability, and drift under field conditions.
• Optimize inference workflows for low-connectivity and resource-constrained environments.
• Support development of multimodal pipelines linking speech, transcripts, metadata, and field images collected through SIKIA.
• Implement workflows for multimodal data validation, synchronization, storage, annotation, and dataset versioning.
• Support training, deployment, and evaluation of multimodal and visual-language AI models.
• Develop scalable workflows for managing multimodal datasets and model outputs across cloud infrastructure.
• Support benchmarking, reproducibility, and optimization of multimodal AI pipelines for field deployment.
• Support development and deployment of AI workflows for disease detection and severity scoring using field images and multimodal data.
• Implement data and evaluation pipelines for disease annotation, validation, benchmarking, and continuous model improvement.
• Support integration of disease scoring workflows within the SIKIA and ONA platforms for field-based data collection and analysis.
• Develop CI/CD pipelines for model training, evaluation, and deployment.
• Manage experiment tracking, model registries, and dataset versioning workflows.
• Implement monitoring and logging across ML services.
• Collaborate with the software development team to integrate RAG pipelines into an agentic deployment structure.
• Support deployment of ML services on GCP, FAIRGrounds, and related infrastructure.
• Support integration of ML services within the SIKIA platform across mobile, backend, API, and cloud systems.
• Ensure compliance with data governance, security, and responsible AI requirements.

• Experience building and managing ML workflows, including model training, deployment, monitoring, and versioning.
• Strong programming skills in Python.
• Familiarity with ML frameworks such as PyTorch or TensorFlow.
• Experience working with cloud platforms such as GCP, AWS, or Azure.
• Strong experience with NLP, speech technologies, conversational AI, or LLM-based applications.
• Experience with multimodal AI, computer vision, or image-based AI workflows.
• Ability to work collaboratively across technical, product, and field teams.
• Strong communication, documentation, and problem-solving skills.

• Master’s degree in Computer science, Data science, Artificial intelligence, Software engineering, or a related field.