Senior AI Engineer – GenAI & Agentic Systems
Provectus
If you've built ML models and want to move into production-grade generative AI work early in your career, this senior role at Provectus is a direct path forward. You'll work on real systems using LLMs, RAG architecture, and agentic systems — skills that are still rare and in high demand.
As a Senior AI Engineer, you'll design and deploy cutting-edge AI solutions across the company's cloud and data engineering stack. Your day-to-day involves hands-on Python development, working with AWS Bedrock, building well-structured production code (not just notebooks), and solving actual classification, regression, and NLP problems. You'll own projects in LLMs, recommendation engines, or other GenAI use cases, and you'll do it all remotely.
This fits you if you have strong fundamentals in ML algorithms and math, practical experience shipping models to production, solid Python and Docker skills, and the ability to communicate clearly with teams. A background in computer science, math, physics, or related fields helps. Portfolio projects or real work samples showing production ML experience will stand out.
To apply, submit your resume and a brief note about your production ML experience on CareerJumpShip. Include links to relevant projects or GitHub work if you have them.
About this role
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 upper- 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. Originally posted on Himalayas
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