Back to jobs

Machine Learning and Operations Consultancy

Arusha, Arusha, Tanzania

Live, first seen by us 26 days ago
Pay
Pay not listed
Work setting
Not stated
Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting

What you’ll work on

Full posting
  • Build CI/CD pipelines for model training, evaluation and deployment

  • Manage experiment tracking, model registries and dataset versioning

  • Implement automated retraining pipelines using newly collected field data

  • Monitor model performance, latency, reliability and drift in the field

  • Support disease detection and severity scoring workflows in SIKIA and ONA

From the employer’s posting
Develop CI/CD pipelines for model training, evaluation, and deployment.
Manage experiment tracking, model registries, and dataset versioning workflows.
Implement automated retraining and fine-tuning pipelines using newly collected field data.
Monitor model performance, latency, reliability, and drift under field conditions.
Support integration of disease scoring workflows within the SIKIA and ONA platforms for field-based data collection and analysis.

Posting record

Report an error
Status in our records
Live
First seen by us
Oct 10, 202626 days ago
Recorded sightings
27
Last seen by us
Nov 5, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 26

Similar jobs usually come down between day 6 and day 70.

From 4,444 similar postings that came down. Coming down is not always a hire.

Bioversityinternational is hiring

3 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • AWS
  • Azure
  • GCP
  • PyTorch
  • TensorFlow
Where the posting names them
• Strong programming skills in Python and familiarity with ML frameworks such as PyTorch or TensorFlow.
• Experience working with cloud platforms such as GCP, AWS, or Azure.

Your next step

Complete your application on bioversityinternational.zohorecruit.eu. The employer’s form will show what is required.

Already applied? Track this application

Demand for ML Engineer roles

New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.

Weekly new ML Engineer postings at the same employers, from the week of August 17 to the week of October 26. The fewest in a week was 625, the most 1,094, and the latest week had 876.
August 17Last week

Each bar is one week of new postings. The darkest bars are the last 5 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

THE ORGANIZATION The Alliance of Bioversity International (www.bioversityinternational.org) and the International Center for Tropical Agriculture (CIAT) (www.ciat.cgiar.org) delivers research-based solutions that harness agricultural biodiversity and sustainably transform food systems to improve people’s lives. Alliance solutions address the global crises of malnutrition, climate change, biodiversity loss, and environmental degradation. With novel partnerships, the Alliance generates evidence and mainstreams innovations to transform food systems and landscapes so that they sustain the planet, drive prosperity, and nourish people in a climate crisis. The Alliance is part of CGIAR, a global research partnership for a food-secure future. Background: The NDIZI (NLP to Develop and Innovate Zero-shot Intelligence) project seeks a dedicated Machine Learning Operations (MLOps) Consultant (“the consultant”) to support the development, deployment, and operationalization of machine learning systems powering SIKIA, a voice-first and multimodal AI platform for conversational data collection and analysis, and 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, scalable solutions suitable for real-world field deployment. We are building these systems to enable crop improvement systems to bridge formal breeding processes happening within controlled experimental trials with real-world environments on-farm. Principal use-cases include identifying farmer preferences, assessing plant disease occurrence and scoring, and environmental response - particularly related to 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 spanning data ingestion, validation, dataset versioning, model training, evaluation, deployment, monitoring, and continuous improvement across 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 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, and FAIRGrounds-integrated systems. In addition, the role will contribute to strengthening best practices around experiment tracking, model governance, CI/CD workflows, deployment automation, and ML system monitoring across NDIZI infrastructure. This is a 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 ● Support 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 ● 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 ● 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 ● 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 Work plan (Month 1, ~Week 4) Develop an inception report outlining the technical approach, deployment priorities, infrastructure requirements, integration roadmap, and detailed 11-month work plan for MLOps, speech, multimodal, and disease-scoring workflows across the SIKIA platform. Honoraria for Deliverable 1: 12,000,000 Tanzanian Shillings/ 589,762 Kenyan Shillings. Deliverable 2: Model Development, Evaluation, and Infrastructure Setup (Month 5, ~Week 20) Establish core MLOps infrastructure and workflows, including CI/CD pipelines, experiment tracking, dataset versioning, model registries, and monitoring systems. Support development, evaluation, and optimization of speech, multimodal, and disease-scoring models across cloud and edge environments. Honoraria for Deliverable 2: 18,000,000 Tanzanian Shillings/ 884,642Kenyan Shillings. Deliverable 3: SIKIA AI Pipeline Integration and Deployment (Month 8, ~Week 32) Deliver integrated deployment workflows connecting ML services with SIKIA mobile applications, APIs, backend systems, and FAIRGrounds-integrated infrastructure. Provide operational pipelines for speech processing, conversational analysis, multimodal workflows, and disease-scoring services, including deployment documentation and integration support. Honoraria for Deliverable 3: 22,000,000 Tanzanian Shillings/ 1,081,230 Kenyan Shillings. Deliverable 4: Monitoring, Evaluation, and Optimization Framework (Month 10, ~Week 40) 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. Provide recommendations for optimization, scalability, and field deployment improvements. Honoraria for Deliverable 4: 16,000,000 Tanzanian Shillings/ 786,349 Kenyan Shillings. Deliverable 5: Final Technical Report and Handover Package (Month 11, end of assignment) Submit a final technical report summarizing completed workflows, deployed infrastructure, system performance, key learnings, and recommendations for future scaling and maintenance. Deliver finalized documentation, deployment guides, pipeline configurations, and knowledge-transfer materials for internal teams. Honoraria for Deliverable 5: 22,000,000 Tanzanian Shillings/ 1,081,230 Kenyan Shillings. Requirements Education: Master’s degree in computer science, Data Science, Artificial Intelligence, Software Engineering, or a related field. Experience: At least 3 years of experience in Machine Learning engineering, MLOps, or deployment of AI systems. Technical Competencies: • Experience building and managing ML workflows, including model training, deployment, monitoring, and versioning. • Strong programming skills in Python and 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 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 will be an added advantage. Benefits Terms of employment This is a nationally recruited position based in either Kenya or Tanzania. The initial contract will be for up to 11 Months, with the total estimated consultancy amount of 90,000,000Tanzanian Shillings or 4,423,212 Kenyan Shillings. Applications Applicants are invited to visit https://www.bioversityinternational.org/jobs/ to get full details of the position and to submit their applications. Applications MUST include reference number Ref: MACHINE LEARNING AND OPERATIONS CONSULTANCY as the position applied for. Application including CV, technical proposal and financial proposal should be saved as one document using the candidate’s last name, first name for ease of sorting. Note: The Alliance does not charge a fee at any stage of the recruitment process (application, interview meeting, processing or training). The Alliance also does not concern itself with information on applicants' bank accounts. Applications closing date: 30 October 2026 Please note that email applications will not be considered. Only short-listed candidates will be contacted. We invite you to learn more about us at: http://alliancebioversityciat.org
Source details

View original posting ↗

Source notes

Source excerpts

Selected passages from the saved posting. Check the full description for conditions and exceptions.

Pay

No pay amount identified in the saved description.

Location & working pattern

Arusha, Arusha, Tanzania

Working pattern and location restrictions need checking in the full posting.

Work authorization

No clear work-authorization passage found. Eligibility is unconfirmed.

Find answers in the posting

AI
How answers work

AI selects complete passages from this posting. Check them for conditions and exceptions.

Uses this posting and your question. No profile needed.