Jobs at Provectus

Three-month unpaid hybrid internship in Odesa building production-ready generative AI systems. Work on agentic systems and RAG pipelines, develop Python backends, integrate top-tier LLMs (OpenAI, Anthropic, AWS Bedrock), use Git and Docker, handle real data, and receive mentorship with potential full-time offers.

Three-month unpaid, on-site internship building production-grade GenAI solutions for financial clients: develop agentic systems and RAG pipelines, build backends with FastAPI/Flask, integrate top LLMs, handle large-scale data migrations, and work with mentors with potential full-time offers.

Three-month on-site AI engineering internship building production-ready Generative AI solutions. Work on agentic systems and RAG pipelines, develop Python backends, integrate top-tier LLMs, handle large-scale data processing, and collaborate with mentors. Successful interns may receive full-time offers as Associate or Junior AI Engineer.

Design and implement cloud-native AI solutions, build relationships with clients, and optimize AI systems. Mentor others and lead architecture decisions.

Design and deliver production-grade cloud-native LLM and agentic AI solutions: implement RAG, deploy and monitor models, build Python backends and ETL pipelines, lead architecture and presales activities, and mentor engineering teams.

Partner with managers across LATAM and North America to run HR operations, compliance (SOC/ISO support), performance management, benefits administration, employee engagement, retention, and full-cycle recruitment. Support employee development, build talent pipelines, and ensure consistent HR communication and processes to foster an inclusive, productive workplace.

Manage full-cycle recruitment across LATAM and North America; partner with hiring managers to define needs and craft talent strategies; source, interview, extend offers, and build talent pipelines; ensure excellent candidate experience; use data to improve recruiting and provide market intelligence; foster university and institutional relationships.

Design and deliver cloud-native AI and LLM-based solutions from discovery through production. Lead presales, architecture, RAG and agentic implementations, deploy and operate ML/LLM systems, write production-grade Python, build ETL/ELT pipelines, implement MLOps/LLMOps/AgentOps, mentor engineers, and manage client technical relationships.

The AI Enablement Lead facilitates AI adoption in enterprises, delivering training programs, coaching sessions, and developing curriculum while engaging closely with customer leadership.

The role involves creating and improving ML models, collaborating with teams, developing experimentation roadmaps, and ensuring optimal performance of production models.

Lead the development of Python services and Generative AI solutions, while mentoring engineers and managing client engagements from discovery to delivery.

As a Machine Learning Solution Architect, design scalable data architectures, lead complex customer engagements, manage technical risks, and mentor teams, focusing on AI/ML solutions in cloud environments.

As an AI/ML Solutions Architect, lead pre-sales discussions, design ML architectures, ensure client satisfaction, and guide technical teams while collaborating on ML solutions.

The role involves technical leadership, mentorship, and hands-on work in machine learning projects, ensuring architectural soundness, mentoring engineers, and advanced coding practices.

The ML Tech Lead will guide the ML engineering team in technical decisions and mentorship, ensuring code quality and facilitating growth in ML competencies while maintaining hands-on coding involvement.

Design, develop, and deploy machine learning solutions iteratively, mentor junior engineers, and build ML accelerators for Provectus clients.

The Senior Solution Owner will lead product strategy and execution for AI and data solutions, overseeing product lifecycles and data migration initiatives, and ensuring effective cross-functional collaboration.

As an ML Solutions Architect, lead technical discussions, design ML architectures, ensure scalability and feasibility, and build client relations. Responsible for architecting solutions using generative AI and overseeing project execution.

The Senior ML Engineer will create and improve ML models, collaborate with teams, monitor models in production, and maintain documentation.

Design and implement ML solutions, build scalable pipelines, mentor engineers, collaborate on projects, and contribute to ML practices. Stay updated on ML research and propose improvements.