IT Expert / AI & ML Engineer Job Grey Edge Monitoring

IT Jobs. Grey Edge Monitoring Jobs

Grey Edge Monitoring (GEM) is a Nairobi-based agri-intelligence company operating across Kenya. GEM’s flagship product, the Intelligent Farmer platform, is powered by the Grey Intelligence AI engine and Joyce AI a multilingual agronomist chatbot serving smallholder farmers and agri-institutions across seven role-based portals. GEM operates on-premise infrastructure including dual NVIDIA A100 80GB GPUs, 512GB RAM, and 26.88TB NVMe storage.

Role Overview

The IT Expert / AI & ML Engineer is GEM’s core AI practitioner. This role owns the Grey Intelligence engine the AI backbone underpinning crop yield prediction, market intelligence, climate risk modelling, and farmer advisory outputs across the Intelligent Farmer platform. The successful candidate will combine deep machine learning engineering expertise with the IT infrastructure capability to maintain, optimize, and extend GEM’s on-premises GPU compute environment. A formal dotted-line relationship with the Chief Agronomist ensures that model development is grounded in real agricultural domain knowledge, not only in algorithmic performance. This is a fulltime job based in Nairobi, Kenya.

Key Responsibilities

AI & Machine Learning Engineering

• Design, train, evaluate, and deploy machine learning models that power GEM’s core intelligence products: crop yield prediction, input recommendation, disease and pest risk scoring, market price forecasting, and climate anomaly detection.
• Grey Intelligence engine architecture: model versioning, feature stores, training pipelines, inference optimisation, and performance monitoring in production.
• Develop and maintain ML pipelines using GEM’s stack (Python, TensorFlow/PyTorch) on GEM’s on-premise NVIDIA A100 GPU infrastructure.
• Collaborate with Data Engineers to ensure high-quality, well-structured training data from partner data feeds, and farmer input logs.
• Develop NLP and language model capabilities to support Joyce AI’s agronomic dialogue trees, multilingual response generation, and conversational reasoning across 30+ languages.
• Conduct model explainability and bias audits to ensure AI outputs are trustworthy, equitable, and fit for use by smallholder farmers with varying literacy levels.

IT Infrastructure & MLOps

• Administer and maintain GEM’s on-premise server infrastructure (Dell PowerEdge R750, dual NVIDIA A100 80GB GPUs, 512GB RAM, 26.88TB NVMe) including OS, drivers, and GPU health monitoring.
• Design and manage MLOps workflows: experiment tracking (MLflow/W&B), model registry, CI/CD for model deployment, and infrastructure-as-code for reproducibility.
• Maintain and optimise GEM’s databases in the context of AI workloads: PostgreSQL/PostGIS for spatial features, TimescaleDB for time-series inputs, Redis for feature caching and inference serving.
• Manage compute resource allocation across training, inference, and platform workloads; implement GPU scheduling and cost governance.
• Ensure system reliability, backups, and disaster recovery protocols for GEM’s AI infrastructure.
• Support the Cybersecurity Lead on AI-specific security considerations including model access controls, data pipeline security, and adversarial input handling.

Agricultural AI & Domain Integration

• Maintain a formal working relationship with the Chief Agronomist and agronomists to ground AI model design in agronomic reality translating field knowledge into feature engineering, label definitions, and output interpretation.
• Collaborate with the Agricultural Intelligence Specialist to build and maintain the agricultural knowledge bases that power Joyce AI’s advisory content.
• Develop crop-specific models for GEM’s primary commodities (coffee, cocoa, maize, beans) with accuracy thresholds appropriate for farmer advisory and financial institution risk scoring.
• Contribute to GEM’s climate intelligence layer integrating weather station data, satellite climate indices, and seasonal forecasts into actionable agronomic risk outputs.

Platform & Product Integration

• Work with the developers to produce conversational AI capabilities ensuring model outputs are formatted, constrained, and monitored for safe deployment in farmer-facing dialogue.
• Expose AI model outputs via internal APIs consumed by GEM’s seven platform portals.
• AI feature scoping, feasibility assessments, and technical documentation for platform roadmap planning.
• Contribute to GEM’s regulatory and compliance intelligence layer, particularly AI-driven EUDR risk scoring for coffee and cocoa supply chains.

Qualifications & Experience

• Bachelor’s degree or higher in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a closely related field.
• 3+ years of hands-on machine learning engineering experience and model development through production deployment, not only notebook-level experimentation.
• Proficiency in Python and the ML ecosystem: scikit-learn, TensorFlow or PyTorch, Pandas, NumPy, and model serving frameworks (FastAPI, TorchServe, or equivalent).
• Demonstrable experience administering Linux servers and GPU compute environments (CUDA, cuDNN, NVIDIA driver management).
• Experience with MLOps tooling: experiment tracking, model versioning, CI/CD pipelines for ML, and container-based deployment (Docker, Kubernetes or equivalent).
• Solid understanding of relational databases (PostgreSQL) and time-series data (TimescaleDB or InfluxDB) in the context of ML feature engineering.
• Experience with NLP, large language models, or conversational AI systems is strongly preferred.

PREFERRED QUALIFICATIONS

• Experience with Google Genkit or other LLM orchestration frameworks.
• Familiarity with geospatial features and spatial machine learning (PostGIS, Rasterio, satellite imagery feature extraction).
• Working knowledge of agricultural data crop physiology, soil science, climate indices, or agronomic decision models either through prior experience or a demonstrated ability to learn rapidly from domain experts.
• Exposure to fairness-aware ML, low-resource NLP, or AI for low-connectivity / low-literacy user contexts.
• Experience with Redis for caching and feature serving at inference time.
• Familiarity with GEM’s broader stack: Next.js, Firebase, Node.js, React/TypeScript.
• Published work, open-source contributions, or portfolio demonstrating applied ML in production environments.

PREFERRED SKILLS

The successful candidate will be based in Nairobi, Kenya. The following competences are essential;

• AI depth: commands the full ML development lifecycle from problem framing and data curation through model training, evaluation, and production monitoring.
• IT ownership: takes responsibility for the health and performance of GEM’s compute infrastructure, treats uptime as a professional obligation not a secondary concern.
• Domain curiosity: actively learns from agronomists and farmers; understands that model performance in the lab means nothing if outputs don’t translate to real agricultural decisions.
• Systems thinking ; designs AI systems that integrate cleanly with GEM’s platform, data infrastructure, and compliance requirements.
• Communication: able to translate model performance metrics into business-relevant language for GEM leadership, institutional partners, and development finance evaluators

HOW TO APPLY

Send your CV, a brief cover letter, and a portfolio or GitHub link demonstrating applied ML work to info@greyedgemonitoring.com with the subject line: IT Expert / AI & ML Engineer — [Your Name]. Please note that only short-listed applicants meeting the above requirements will be contacted.