As an ML Engineer at Provectus, you'll create and improve ML models, collaborate on production efforts, develop experimentation roadmaps, and maintain model performance.
Join us at Provectus to be a part of a team that is dedicated to building cutting-edge technology solutions that have a positive impact on society. Our company specializes in AI and ML technologies, cloud services, and data engineering, and we take pride in our ability to innovate and push the boundaries of what's possible.
As an ML Engineer, you’ll be provided with all opportunities for development and growth.
Let's work together to build a better future for everyone!
Requirements:
- Comfortable with standard ML algorithms and underlying math.
- Strong hands-on experience with LLMs in production, RAG architecture, and agentic systems
- AWS Bedrock experience strongly preferred
- Practical experience with solving classification and regression tasks in general, feature engineering.
- Practical experience with ML models in production.
- Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines.
- Solid software engineering skills (i.e., ability to produce well-structured modules, not only notebook scripts).
- Python expertise, Docker.
- English level - strong Intermediate.
- Excellent communication and problem-solving skills.
Will be a plus:
- Practical experience with cloud platforms (AWS stack is preferred, e.g. Amazon SageMaker, ECR, EMR, S3, AWS Lambda).
- Practical experience with deep learning models.
- Experience with taxonomies or ontologies.
- Practical experience with machine learning pipelines to orchestrate complicated workflows.
- Practical experience with Spark/Dask, Great Expectations.
Responsibilities:
- Create ML models from scratch or improve existing models.
- Collaborate with the engineering team, data scientists, and product managers on production models.
- Develop experimentation roadmap.
- Set up a reproducible experimentation environment and maintain experimentation pipelines.
- Monitor and maintain ML models in production to ensure optimal performance.
- Write clear and comprehensive documentation for ML models, processes, and pipelines.
- Stay updated with the latest developments in ML and AI and propose innovative solutions.
Top Skills
AI
AWS
Cloud Services
Dask
Data Engineering
Docker
Llms
Ml
Nlp
Python
Rag Architecture
Recommendation Engines
Spark
Similar Jobs
Artificial Intelligence • Information Technology • Consulting
As an ML Engineer, you will develop ML models, collaborate with teams, maintain model performance, and drive innovation in ML technologies.
Top Skills:
AWSDaskDockerLlmsMl AlgorithmsNlpPythonRag ArchitectureSpark
Artificial Intelligence • Information Technology • Consulting
As an ML Engineer, create and improve ML models, collaborate with teams, maintain experimentation pipelines, and stay updated with ML advancements.
Top Skills:
AWSDaskDockerPythonSpark
Artificial Intelligence • Healthtech • Information Technology • Software • Conversational AI • Generative AI • Automation
As a Senior Software Engineer at Collectly, you will develop core product features, manage product requirements, coding, testing, and maintenance while collaborating with the team.
Top Skills:
FlaskPostgresPythonReactRedisSqlalchemy
What you need to know about the Colorado Tech Scene
With a business-friendly climate and research universities like CU Boulder and Colorado State, Colorado has made a name for itself as a startup ecosystem. The state boasts a skilled workforce and high quality of life thanks to its affordable housing, vibrant cultural scene and unparalleled opportunities for outdoor recreation. Colorado is also home to the National Renewable Energy Laboratory, helping cement its status as a hub for renewable energy innovation.
Key Facts About Colorado Tech
- Number of Tech Workers: 260,000; 8.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Lockheed Martin, Century Link, Comcast, BAE Systems, Level 3
- Key Industries: Software, artificial intelligence, aerospace, e-commerce, fintech, healthtech
- Funding Landscape: $4.9 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Access Venture Partners, Ridgeline Ventures, Techstars, Blackhorn Ventures
- Research Centers and Universities: Colorado School of Mines, University of Colorado Boulder, University of Denver, Colorado State University, Mesa Laboratory, Space Science Institute, National Center for Atmospheric Research, National Renewable Energy Laboratory, Gottlieb Institute
