3 дня назад
ML Engineer – Generative AI & LLMs (Remote)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
ML Engineer – Generative AI & LLMs (Remote) (Generative AI/LLMs): Designing, prototyping, and productizing scalable machine learning solutions with an accent on LLM engineering, real-world data, and production-ready services. Focus on building agentic systems, rapidly turning research ideas into deployable tools, and applying Python-based machine learning to business and product outcomes.
Location: Remote. The company is based at 720 King St W, Toronto, Canada.
Company
is a Toronto-based consulting firm providing engineering and data capabilities to startups and Fortune 500 clients.
What you will do
- Design, prototype, and productize scalable machine learning solutions using LLMs and real-world data.
- Build and improve LLM-powered systems and explore agentic capabilities such as tool use, memory, planning, and multi-agent coordination.
- Rapidly prototype emerging tools and research ideas, then turn successful prototypes into production-ready services.
- Collaborate closely with clients, engineers, and data scientists on open-ended technical and business problems.
- Assess data quality and its impact on product and business outcomes.
Requirements
- Expertise in LLM engineering, including familiarity with major LLM providers and their best practices.
- Experience with statistics and machine learning, plus hands-on proficiency with the Python ML stack, including Pandas, NumPy, scikit-learn, XGBoost, and PyTorch.
- Proficiency with Git, Docker, SQL, Bash, and FastAPI.
- Ability to prototype quickly and deliver production-ready services.
- Strong analytical thinking and business acumen.
- Bachelor’s degree or higher in Computer Science or a related coding-focused engineering field.
Nice to have
- Experience building agentic systems.
- Familiarity with AWS or Azure, GitHub Actions, Spark, Neo4j Cypher, and graph databases.
Culture & Benefits
- Remote contract position.
- Competitive compensation.
- Collaborative, high-performing environment with an emphasis on accountability and minimal micromanagement.
- Opportunities for continuous learning and work on cutting-edge machine learning systems.
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