Senior ML Engineer

Summary

Join Method, a global design and engineering consultancy, as a hands-on ML Engineer on our Data & AI team. You will design, develop, and optimize machine learning models and LLMs for intelligent automation and decision-making. Collaborate with MLOps engineers and data engineers to build production-ready ML solutions covering the full ML lifecycle. Your work will involve data preparation, feature engineering, model training, evaluation, and deployment, ensuring scalability, performance, and reliability. Travel for team and client meetings is required (up to 15%). Method offers a collaborative environment and competitive perks, including continuing education, flexible PTO, private medical care, and more.

Requirements

• 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

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

Benefits

• 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