Junior Machine Learning Engineer

Description

The Data Science team is looking for a Junior Machine Learning Engineer to design, build, and ship AI-powered solutions — with a strong emphasis on Large Language Model applications. You will be involved in projects end-to-end: from prototyping and experimentation through to production-grade systems serving real users. This is a high-impact role where you will take on real responsibility early and shape how AI is built and delivered across the organization.

Responsibilities

As a Junior Machine Learning Engineer You Will:

• Build LLM-Powered Applications: Design and develop applications leveraging Large Language Models — including RAG systems, agentic workflows, and conversational interfaces — tailored to complex business needs in a regulated environment.
• Develop & Fine-Tune Models: Train, fine-tune, and evaluate both classical ML and language models, selecting the right approach for each problem and optimizing for production constraints such as latency, cost, and accuracy.
• Engineer for Production: Build reliable, scalable ML services and APIs. You care about code quality, testing, and maintainability as much as model performance.
• Evaluate & Iterate: Contribute to evaluation frameworks for AI applications — particularly for generative systems where traditional metrics fall short — and use them to drive continuous improvement.
• Stay on the Cutting Edge: Actively track the fast-moving LLM landscape, assess emerging tools and techniques (new model releases, orchestration frameworks, prompting strategies), and translate them into practical value for the team.
• Collaborate Across Teams: Work closely with data scientists and MLOps engineers, product owners, and business stakeholders to translate business problems into well-scoped AI solutions.

Qualifications

• Educational Background: Degree in Computer Science, Machine Learning, Data Science, Engineering, Mathematics, or a related field.
• Python Proficiency: Strong programming skills in Python. You write clean, maintainable code and care about engineering best practices.
• LLM & NLP Experience: Hands-on experience building applications with Large Language Models (prompt engineering, RAG, fine-tuning, agent frameworks) — whether through professional experience, personal projects, or academic work.
• ML Fundamentals: Solid grounding in classical machine learning and deep learning. You can pick the right tool for the job, whether that's a gradient-boosted tree or a transformer.
• Full-Stack Comfort: You are able to build beyond the model — whether that means spinning up an API, putting together a web interface, or wiring up a data pipeline.
• Production Mindset: Familiarity with deploying models or applications into production. Experience with containerization (Docker, Kubernetes) and CI/CD is a strong plus.
• Evaluation & Critical Thinking: You understand the challenges of evaluating generative AI systems and can think critically about designing meaningful benchmarks beyond simple accuracy metrics.
• Communication: Fluent in English, able to collaborate effectively with both technical peers and non-technical stakeholders.

Nice to Have

• Experience with MLflow, fine-tuning open-source LLMs, or frontend frameworks (React, Streamlit, Gradio).