Responsibilities
- Collaborate with delivery leaders to scope technical solutions to operational problems
- Identify workflow optimizations through deep engagement with customer problems and work to build into a stable and scalable solution.
- Design and implement AI-powered workflows using LLMs, embedding models, retrieval systems, and automation tools
- Translate messy real-world constraints (e.g., inconsistent data, latency requirements) into elegant engineering solutions
- Iterate quickly based on real-time feedback from operators and clients
- Build reusable tooling and infrastructure that accelerates future deployments.
Requirements
- 1-4 years of professional experience in software engineering or ML engineering
- Strong Python skills and familiarity with libraries like PyTorch, Hugging Face, LangChain, or OpenAI APIs
- Solid understanding of data pipelines, APIs, and production deployment (e.g., Docker, FastAPI, GCP/AWS)
- Experience building usable systems from messy data and ambiguous requirements
- Strong communication skills — you can explain technical decisions to delivery teams and client stakeholders
- Entrepreneurial mindset and comfort working in fast-moving, unstructured environments
- Ability to travel roughly 25–50 % of the time, sometimes short-notice trips—primarily across North America with occasional international roll-outs—to work directly on-site with clients.
- Strong engineering background demonstrated by a Bachelor’s degree in Data Science, Computer Science and related fields OR equivalent professional experience.
Benefits
- Fair and competitive pay
- Compensation reflects both market conditions and the value each team member brings
- Salary structure accounts for regional differences in cost of living while maintaining internal equity
- Bonuses and equity are included in offers above entry level
- Final compensation is determined by a combination of factors, including location, job-related experience, skills, knowledge, internal pay equity, and overall market conditions
Work Arrangement
Hybrid
Additional Information
- Ability to be on-call for our customers when situations arise
- Will be required to travel roughly 25–50 % of the time, sometimes short-notice trips—primarily across North America with occasional international roll-outs—to work directly on-site with clients.
