ML Implementation Engineer

ML First Priority

• Solid understanding of the AI/ML ecosystem: model training, evaluation, deployment, and monitoring in production.
• Hands-on experience across at least one problem type classification, forecasting, NLP, or LLM-based systems end to end.
• Familiarity with LLMs: what they are good at, where they fail, and how to use them responsibly in a business context.
• Ability to reason about which AI approach fits a problem and articulate why alternatives were not chosen.

Python and Data Engineering

• 2-4 years of production Python clean, tested, well-structured code you would defend comfortably in a review.
• Experience building data pipelines that run reliably in production, not just locally.
• Comfortable handling messy, incomplete, or late data — not just clean tutorial datasets.

Deployment and Operations

• Comfortable deploying and running services on Linux servers — independently.
• Experience packaging and shipping code to a server: environment setup, configuration, and service management.
• Basic cloud familiarity — enough to navigate AWS, read logs, and understand where things run.

Good to Have

• MLOps practices: model versioning, drift monitoring, or automated retraining pipelines.
• CI/CD exposure — automated testing and deployment.
• Domain exposure in agri-tech, FMCG, dairy, or food supply chain.

We are an AWS-first team. Beyond that, we are pragmatic we choose tools that fit the problem, not tools that sound impressive. You will be expected to have opinions on this.

Core Be Comfortable Here

• Python the primary language for everything: model code, pipelines, APIs, and deployment scripts.
• ML libraries scikit-learn, pandas, NumPy, and at least one deep learning or LLM framework.
• Data pipeline orchestration — experience with any modern workflow tool; the specific choice is open.
• Server deployment — packaging, configuring, and running services on remote Linux environments.
• Docker — containerisation for reproducible, portable model and service deployments.
• REST APIs — building and consuming them; FastAPI or equivalent Python framework.
• Linux — SSH, bash, process management, log reading; you work on servers, not just laptops.
• Git — branching, pull requests, and meaningful code review.

Relevant — Exposure or Willingness to Learn

• Cloud storage and compute — AWS S3, EC2, Lambda, or equivalent managed services.
• Cloud AI/ML platforms — AWS SageMaker, Amazon Bedrock, or equivalent.
• LLM APIs — OpenAI, Bedrock, or similar; prompt engineering and structured output handling.
• Time-series forecasting libraries — Prophet, statsmodels, or equivalent.
• Vector databases — for RAG and semantic retrieval workflows.
• Optimisation libraries — for constraint-based decision and scheduling problems.
• CI/CD basics — automated testing pipelines and deployment automation.