Senior AI ML Engineer - Data Science & AI

Genesys empowers organizations of all sizes to improve loyalty and business outcomes by creating the best experiences for their customers and employees. Through Genesys Cloud, the AI-powered Experience Orchestration platform, organizations can accelerate growth by delivering empathetic, personalized experiences at scale to drive customer loyalty, workforce engagement, efficiency and operational improvements.

We employ more than 6,000 people across the globe who embrace empathy and cultivate collaboration to succeed. And, while we offer great benefits and perks like larger tech companies, our employees have the independence to make a larger impact on the company and take ownership of their work. Join the team and create the future of customer experience together.

Senior AI/ML Engineer - Data Science & AI

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 Senior 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

· Design and implement novel approaches to building specialised Large Language Models, working directly with model weights, attention mechanisms, and efficient sparse architectures. You'll translate cutting-edge research papers into working implementations.

· Own the full 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 daily.

· Establish rigorous evaluation frameworks to compare model variants across intelligence, reasoning capability, and inference cost. You'll design experiments, build comparison tooling, and present findings to stakeholders to guide strategic decisions.

· Build and maintain the cloud infrastructure (AWS CloudFormation, SageMaker, S3, GPU instances) that powers our experimentation and model serving. You'll optimise for both research velocity and cost efficiency.

· Engage with the open-source ML ecosystem, leveraging and contributing to established toolkits for model development and evaluation.

· Guide junior engineers, lead design discussions, and help set the technical direction for the team's research agenda.

Required Skills & Experience

· Degree in Computer Science, Machine Learning, Mathematics, or a related quantitative field (or equivalent hands-on experience). 5+ years in ML engineering, with at least 2 years working directly with LLMs or deep learning model architectures.

· Strong understanding of transformer architectures, attention mechanisms, and model internals. You should be comfortable reasoning about parameter counts, tensor shapes, weight distributions, and how architectural choices affect model behaviour.

· Expert-level Python with deep hands-on experience in PyTorch. Familiarity with HuggingFace Transformers, tokenizers, model loading/saving, and the broader open-weight model ecosystem (Llama, Mistral, DeepSeek, and similar model families).

· Hands-on experience with AWS services for ML workloads — SageMaker (Notebooks/Studio), EC2 GPU instances, S3 for model storage, and CloudFormation or IaC for reproducible deployments.

· Clean code practices, Git workflows, CI/CD (Jenkins or similar), unit testing, and the ability to build well-structured Python packages — not just notebooks.

Desirable Skills & Experience

The following would strengthen your application but are not required:

· Experience with sparse architectures, expert routing, conditional computation, or techniques for adapting and combining pre-trained models to create new, specialised variants.

· Experience with quantization (GPTQ, AWQ, bitsandbytes), model compression, multi-GPU parallelism, or distributed inference strategies for running large models efficiently.

· Familiarity with the contact centre industry, conversational AI, or agentic AI systems is a plus but not essential — we'll teach you the domain.

· An awkward sense of humour and a genuine love for digging into model internals. We value curiosity, intellectual honesty, and people who aren't afraid to say "I don't know, but I'll figure it out."

If a Genesys employee referred you, please use the link they sent you to apply.

About Genesys:

Genesys® empowers more than 8,000 organizations worldwide to create the best customer and employee experiences. With agentic AI at its core, Genesys Cloud™ is the AI-Powered Experience Orchestration platform that connects people, systems, data and AI across the enterprise. As a result, organizations can drive customer loyalty, growth and retention while increasing operational efficiency and teamwork across human and AI workforces. To learn more, visit www.genesys.com.

Reasonable Accommodations:

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Genesys is an equal opportunity employer committed to fairness in the workplace. We evaluate qualified applicants without regard to race, color, age, religion, sex, sexual orientation, gender identity or expression, marital status, domestic partner status, national origin, genetics, disability, military and veteran status, and other protected characteristics.

Please note that recruiters will never ask for sensitive personal or financial information during the application phase.