Founding Ml Engineer (Spectrum)

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!RequirementsComfortable with standard ML algorithms and underlying mathStrong hands?on experience with LLMs in production, RAG architecture, and agentic systemsAWS Bedrock experience strongly preferredPractical experience with solving classification and regression tasks in general, feature engineeringPractical experience with ML models in productionPractical experience with one or more use cases from the following: NLP, LLMs, and Recommendation enginesSolid software engineering skills (i.E., ability to produce well?structured modules, not only notebook scripts)Python expertise, DockerEnglish level - strong upper?intermediateExcellent communication and problem?solving skillsWill be a plusPractical experience with cloud platforms (AWS stack is preferred, e.G., Amazon SageMaker, ECR, EMR, S3, AWS Lambda)Practical experience with deep learning modelsExperience with taxonomies or ontologiesPractical experience with machine learning pipelines to orchestrate complicated workflowsPractical experience with Spark/Dask, Great ExpectationsResponsibilitiesCreate 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.We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses.These tools assist our recruitment team but do not replace human judgment.Final hiring decisions are ultimately made by humans.If you would like more information about how your data is processed, please contact us.#J-*****-Ljbffr

RequirementsComfortable with standard ML algorithms and underlying mathStrong hands?on experience with LLMs in production, RAG architecture, and agentic systemsAWS Bedrock experience strongly preferredPractical experience with solving classification and regression tasks in general, feature engineeringPractical experience with ML models in productionPractical experience with one or more use cases from the following: NLP, LLMs, and Recommendation enginesSolid software engineering skills (i.E., ability to produce well?structured modules, not only notebook scripts)Python expertise, DockerEnglish level - strong upper?intermediateExcellent communication and problem?solving skillsWill be a plusPractical experience with cloud platforms (AWS stack is preferred, e.G., Amazon SageMaker, ECR, EMR, S3, AWS Lambda)Practical experience with deep learning modelsExperience with taxonomies or ontologiesPractical experience with machine learning pipelines to orchestrate complicated workflowsPractical experience with Spark/Dask, Great ExpectationsResponsibilitiesCreate 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.We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans.

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.