As an ML Engineer, create and improve ML models, collaborate with teams, maintain experimentation pipelines, and stay updated with ML advancements.
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
AWS
Dask
Docker
Python
Spark
Similar Jobs
Artificial Intelligence • Information Technology • Consulting
The ML Engineer will build and refine ML models, manage experimentation environments, collaborate with teams, and ensure model performance in production.
Top Skills:
Amazon SagemakerAws BedrockAws StackDaskData PipelinesDockerLlmsMl AlgorithmsPythonSpark
Cloud • Security • Software • Cybersecurity • Automation
The Product Design Manager leads UX efforts for GitLab's Plan stage, focusing on team management, strategic user research, product design, and enhancing user experience across tools.
Top Skills:
AgileAIGitlabUx
Cloud • Security • Software • Cybersecurity • Automation
As a Customer Success Engineer, you'll provide technical guidance to newly onboarded customers, drive product adoption, and collaborate with teams to ensure customer satisfaction and success with GitLab's platform.
Top Skills:
Agile PlanningCdCiDevsecops ToolsGitlabScm
What you need to know about the Los Angeles Tech Scene
Los Angeles is a global leader in entertainment, so it’s no surprise that many of the biggest players in streaming, digital media and game development call the city home. But the city boasts plenty of non-entertainment innovation as well, with tech companies spanning verticals like AI, fintech, e-commerce and biotech. With major universities like Caltech, UCLA, USC and the nearby UC Irvine, the city has a steady supply of top-flight tech and engineering talent — not counting the graduates flocking to Los Angeles from across the world to enjoy its beaches, culture and year-round temperate climate.
Key Facts About Los Angeles Tech
- Number of Tech Workers: 375,800; 5.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Snap, Netflix, SpaceX, Disney, Google
- Key Industries: Artificial intelligence, adtech, media, software, game development
- Funding Landscape: $11.6 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Strong Ventures, Fifth Wall, Upfront Ventures, Mucker Capital, Kittyhawk Ventures
- Research Centers and Universities: California Institute of Technology, UCLA, University of Southern California, UC Irvine, Pepperdine, California Institute for Immunology and Immunotherapy, Center for Quantum Science and Engineering