Job Description
Method, a global design and engineering consultancy, is seeking a highly experienced ML Staff Engineer to join their Data & AI team. The ideal candidate will design, develop, and deploy AI applications incorporating sophisticated AI agents capable of complex reasoning, planning, and autonomous action. This role requires hands-on expertise in Python and the ability to architect agentic workflows and integrate complex ETL pipelines.He/She will tackle ambiguous problems, provide technical leadership across front-end, back-end, and AI components, and help shape the technical direction for Method's agentic AI initiatives. This is a unique opportunity to apply full-stack expertise to the forefront of AI development. Travel for team and client meetings is required, typically up to 15%.
- Develop and manage AI-based data pipelines.
- Work with foundation models and unstructured data.
- Optimize AI model inference for speed and cost-effectiveness.
- Evaluate AI models, ensuring appropriate tone and content.
- Apply prompt engineering techniques to guide AI models.
- Develop AI-powered interfaces.
Requirements: - Experience in data engineering (Pandas, NumPy, Sklearn).
- DataBase interfacing: SQLAlchemy, Alembic.
- 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.
- Understanding of data preprocessing techniques.
- Proven ability to optimize model inference.
- Experience in evaluating AI applications.
- Skill in evaluating open-ended, non-deterministic AI models.
- Proficiency in prompt engineering and context construction.
- Experience developing AI-powered interfaces (Flask, Jinja, FastAPI, Streamlit).
- Knowledge of various AI-related technologies and tools.
Method offers: - Continuing education opportunities
- Flexible PTO and work-from-home policies
- 401K matching
- Health, Dental and Vision benefits, starting on day 1
- Company lunches, company outings, along with a lot of snacks
- Health and wellness programs