Data Science / AI ML Engineer

Be the one building AI-powered experiences where they matter most. At Genesys, we help organizations create better customer experiences through AI-powered experience orchestration. Our platform connects people, systems, data and AI to help organizations deliver more personalized service, improve operational efficiency and build stronger customer relationships. Help build, support and operate technology used by more than 8,000 organizations in over 100 countries – moving AI from possibility to production in real-world enterprise environments every day. AI/ML Engineer The Team and Your Role We're a research-driven team pushing the boundaries of what's possible with Large Language Models. Our work sits at the intersection of applied ML research and production engineering — we're not just consuming off-the-shelf models, we're building our own. We're developing novel approaches to create specialised LLMs that deliver commercial-grade intelligence at a fraction of the cost of frontier API models. We're looking for a AI/ML Engineer who gets excited about model internals — someone who thinks in terms of tensor operations, architecture design, and weight spaces rather than just prompt templates. You'll join a collaborative, diverse team where research ideas move quickly from paper to prototype to production, and where your contributions will directly shape the architecture of next-generation AI systems used by millions. What You Will Do Contribute to the design and implementation of specialised Large Language Models, working with model weights, attention mechanisms, and efficient sparse architectures. You'll help translate research papers into working implementations under the guidance of senior team members. Participate in the lifecycle from research spike through to deployable artefact — reading papers, prototyping in notebooks, building reusable Python libraries, and deploying on GPU-accelerated cloud infrastructure. You'll work with tools like PyTorch, HuggingFace Transformers, and AWS SageMaker regularly. Help build and maintain evaluation frameworks to compare model variants across intelligence, reasoning capability, and inference cost. You'll run experiments, contribute to comparison tooling, and help present findings to stakeholders. Support and extend the cloud infrastructure (AWS CloudFormation, SageMaker, S3, GPU instances) that powers our experimentation and model serving. You'll contribute to optimising for both research velocity and cost efficiency. Engage with the open-source ML ecosystem, leveraging established toolkits for model development and evaluation. Actively participate in design discussions, contribute ideas to the team's research agenda, and provide guidance to more junior team members where appropriate Required Skills & Experience Degree in Computer Science, Machine Learning, Mathematics, or a related quantitative field (or equivalent hands-on experience). 3+ years in ML engineering, with at least 1 year working directly with LLMs or deep learning model architectures. A relevant Master's degree would reduce the experience requirement by 1–2 years. Good understanding of transformer architectures, attention mechanisms, and model internals. You should be able to reason about parameter counts, tensor shapes, and how architectural choices affect model behaviour — and be eager to deepen this knowledge further. Solid proficiency in Python with hands-on experience in PyTorch. Familiarity with HuggingFace Transformers, tokenizers, model loading/saving, and awareness of the broader open-weight model ecosystem (Llama, Mistral, DeepSeek, and similar model families). Familiarity with AWS services for ML workloads — SageMaker (Notebooks/Studio), EC2 GPU instances, and S3 for model storage. Exposure to CloudFormation or other IaC tooling is a plus. Clean code practices, Git workflows, CI/CD (Jenkins or similar), unit testing, and the ability to write well-structured Python code — not just notebooks Desirable Skills & Experi