Method is a global design and engineering consultancy founded in 1999. We believe that innovation should be meaningful, beautiful and human. We craft practical, powerful digital experiences that improve lives and transform businesses. Our teams based in New York, Charlotte, Atlanta, London, Poland, Bengaluru, and remote work with a wide range of organizations in many industries, including Healthcare, Financial Services, Retail, Automotive, Aviation, and Professional Services.

Method is part of GlobalLogic, a digital product engineering company. GlobalLogic integrates experience design and complex engineering to help our clients imagine what’s possible and accelerate their transition into tomorrow’s digital businesses. GlobalLogic is a Hitachi Group Company.

We’re seeking a hands-on ML Engineer to join our Data & AI team You will be responsible for designing, developing and optimising machine learning models and LLMs, that drive intelligent automation and decision making across the platform. Working closely with MLOps engineers and data engineers, you will build production-ready ML solutions that span a variety of use cases. Your work will cover the full ML lifecycle, from data preparation and feature engineering to model training, evaluation, and deployment and ensuring scalability, performance, and reliability in the environment.

Travel for team and client meetings is required, typically up to 15%.

Responsibilities:

Design, build, and deploy scalable machine learning solutions across a range of use cases, spanning structured and unstructured data.
Collaborate closely with MLOps Engineers, Data Engineers, and AI Architects to develop robust, production-ready ML pipelines integrated into the broader platform.
Lead experimentation and model development efforts, selecting appropriate algorithms and evaluation metrics based on business and technical context
Participate in feature engineering, data preprocessing, and dataset curation with a strong focus on reproducibility and version control.
Work within an ecosystem that leverages tools such as JupyterHub, MLflow, Kubernetes, and custom workflows for model training and deployment.
Drive continuous improvements to the model lifecycle through automation, testing, and feedback driven iteration.

Qualifications:

5+ years of experience in ML engineering or software development, with a strong focus on building Machine Learning models.
Able to design models and scripts that integrate smoothly into Argo Workflows or similar ML pipelines.
Must have used MLflow (or a similar tool) to log parameters, metrics, and artifacts.
Experience with LLM development and prompt engineering: using frameworks like Hugging Face Transformers or similar to evaluate, and serve LLMs. A working knowledge of prompt templating, few shot prompting and exporting these models for on prem inference
Familiarity with RAG architecture.
Experience or working knowledge of Vector DBs: one of ChromaDB, Weaviate, Pinecone. Familiarity with foundation models and handling unstructured data. Experience with at least two of LangChain, LangSmith, llamaindex, OpenAI apis, Ollama, HuggingFace Transformers, CrewAI.
Proven ability to optimize model inference for speed and cost-effectiveness,
Able to prepare and export models for on-prem inference, including packaging models and tokenizers.

Why Method?

We look for individuals who are smart, kind and brave. Curious people with a natural ability to think on their feet, learn fast, and develop points-of-view for a constantly changing world find Method an exciting place to work. Our employees are excited to collaborate with dispersed and diverse teams that bring together the best in thinking and making. We champion the ability to listen, and believe that critique and dissonance lead to better outcomes. We believe everyone has the capacity to lead and look for proactive individuals who can take and give direction, lead by example, enjoy the making as much as they do the thinking, especially at senior and leadership levels.

We believe in work/life balance. Seriously. We offer a ton of competitive perks, including:

Continuing education opportunities
Flexible PTO and work-from-home policies
Private medical care (can be extended to your family)
Cafeteria system as part of the Benefit platform
Group life insurance
Creative TAX-deductible cost
Other location specific perks (just ask!)

Next Steps

If Method sounds like the place for you, please submit an application. Also, let us know if you have a presence online with a portfolio, GitHub, Dribbble or other platform.

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