Provectus helps companies adopt ML/AI to transform the ways they operate, compete, and drive value. The focus of the company is on building ML Infrastructure to drive end-to-end AI transformations, assisting businesses in adopting the right AI use cases, and scaling their AI initiatives organization-wide in such industries as Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses. We are seeking a highly skilled Machine Learning (ML) Tech Lead with a strong background in Large Language Models (LLMs) and AWS Cloud services. The ideal candidate will oversee the development and deployment of cutting-edge AI solutions while managing a team of 5-10 engineers. This leadership role demands hands-on technical expertise, strategic planning, and team management capabilities to deliver innovative products at scale. Responsibilities: Leadership & Management Lead and manage a team of 5-10 engineers, providing mentorship and fostering a collaborative team environment; Drive the roadmap for machine learning projects aligned with business goals; Coordinate cross-functional efforts with product, data, and engineering teams to ensure seamless delivery. Machine Learning & LLM Expertise Design, develop, and fine-tune LLMs and other machine learning models to solve business problems; Evaluate and implement state-of-the-art LLM techniques for NLP tasks such as text generation, summarization, and entity extraction; Stay ahead of advancements in LLMs and apply emerging technologies; Expertise in multiple main fields of ML: NLP, Computer Vision, RL, deep learning and classical ML. AWS Cloud Expertise Architect and manage scalable ML solutions using AWS services (e.g., SageMaker, Lambda, Bedrock, S3, ECS, ECR, etc.); Optimize models and data pipelines for performance, scalability, and cost-efficiency in AWS; Ensure best practices in security, monitoring, and compliance within the cloud infrastructure. Technical Execution Oversee the entire ML lifecycle, from research and experimentation to production and maintenance; Implement MLOps and LLMOps practices to streamline model deployment and CI/CD workflows; Debug, troubleshoot, and optimize production ML models for performance. Team Development & Communication Conduct regular code reviews and ensure engineering standards are upheld; Facilitate professional growth and learning for the team through continuous feedback and guidance; Communicate progress, challenges, and solutions to stakeholders and senior leadership. Qualifications: Proven experience with LLMs and NLP frameworks (e.g., Hugging Face, OpenAI, or Anthropic models); Strong expertise in AWS Cloud Services; Strong experience in ML/AI, including at least 2 years in a leadership role; Hands-on experience with Python, TensorFlow/PyTorch, and model optimization; Familiarity with MLOps tools and best practices; Excellent problem-solving and decision-making abilities; Strong communication skills and the ability to lead cross-functional teams; Passion for mentoring and developing engineers.

As a Business Analyst & Data Annotator, you will play a crucial role in gathering and analyzing business requirements, acting as a bridge between stakeholder needs and technical teams. You will also handle the data annotation process, ensuring the production of high-quality, accurately labeled datasets necessary for training machine learning models. This role involves close collaboration with ML engineers, data scientists, and business teams to ensure that data aligns with project goals. Your work will center on translating complex business needs and technical specifications into clear instructions, managing data labeling workflows, and maintaining data quality standards. A junior-level candidate with strong English skills (B2 or higher, ideally C1), attention to detail, and a good understanding of business and technical concepts can be successful in this role, especially when working with reports containing specialized terminology. Responsibilities: Develop and implement detailed guidelines and instructions for data labeling and annotation to ensure consistency and accuracy across datasets; Review and validate labeled data, providing constructive feedback to annotation teams to improve data quality and adherence to project standards; Collaborate with data scientists and ML engineers to prepare, organize, and support the creation of high-quality annotated datasets for model training; Manage the annotation workflow, prioritize tasks, and track progress to ensure timely delivery of labeled data; Maintain high standards of data privacy, security, and compliance throughout all annotation processes; Gather and analyze business requirements, workflows, and terminology to understand data needs and improve annotation processes; Facilitate communication between technical teams and stakeholders by translating complex technical or domain-specific language into clear, accessible instructions and explanations; Offer insights into business processes that could benefit from automation or ML solutions, supporting the design and implementation of such projects; Support continuous improvement of data annotation guidelines, workflows, and overall business analysis practices to enhance efficiency and data quality. Requirements: At least 1 year of experience in the relevant role; Excellent English language skills (B2 level or higher, ideally C1), especially when working with reports containing complex terminology; Strong analytical skills and an understanding of business workflows; Attention to detail and ability to create clear instructions and guidelines for annotation teams; Understanding of data privacy, security standards, and compliance requirements. Nice to Have: Basic knowledge of machine learning concepts and data management principles; Familiarity with ML workflows, data pipelines, and MLOps tools; Experience with cloud platforms such as AWS, GCP, or Azure; Experience with data labeling or annotation; Experience in creating markups for AI; Insurance industry background.

Provectus helps companies adopt ML/AI to transform the ways they operate, compete, and drive value. The focus of the company is on building ML Infrastructure to drive end-to-end AI transformations, assisting businesses in adopting the right AI use cases, and scaling their AI initiatives organization-wide in such industries as Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses. We are seeking a highly skilled Machine Learning (ML) Tech Lead with a strong background in Large Language Models (LLMs) and AWS Cloud services. The ideal candidate will oversee the development and deployment of cutting-edge AI solutions while managing a team of 5-10 engineers. This leadership role demands hands-on technical expertise, strategic planning, and team management capabilities to deliver innovative products at scale. Responsibilities: Leadership & Management Lead and manage a team of 5-10 engineers, providing mentorship and fostering a collaborative team environment; Drive the roadmap for machine learning projects aligned with business goals; Coordinate cross-functional efforts with product, data, and engineering teams to ensure seamless delivery. Machine Learning & LLM Expertise Design, develop, and fine-tune LLMs and other machine learning models to solve business problems; Evaluate and implement state-of-the-art LLM techniques for NLP tasks such as text generation, summarization, and entity extraction; Stay ahead of advancements in LLMs and apply emerging technologies; Expertise in multiple main fields of ML: NLP, Computer Vision, RL, deep learning and classical ML. AWS Cloud Expertise Architect and manage scalable ML solutions using AWS services (e.g., SageMaker, Lambda, Bedrock, S3, ECS, ECR, etc.); Optimize models and data pipelines for performance, scalability, and cost-efficiency in AWS; Ensure best practices in security, monitoring, and compliance within the cloud infrastructure. Technical Execution Oversee the entire ML lifecycle, from research and experimentation to production and maintenance; Implement MLOps and LLMOps practices to streamline model deployment and CI/CD workflows; Debug, troubleshoot, and optimize production ML models for performance. Team Development & Communication Conduct regular code reviews and ensure engineering standards are upheld; Facilitate professional growth and learning for the team through continuous feedback and guidance; Communicate progress, challenges, and solutions to stakeholders and senior leadership. Qualifications: Proven experience with LLMs and NLP frameworks (e.g., Hugging Face, OpenAI, or Anthropic models); Strong expertise in AWS Cloud Services; Strong experience in ML/AI, including at least 2 years in a leadership role; Hands-on experience with Python, TensorFlow/PyTorch, and model optimization; Familiarity with MLOps tools and best practices; Excellent problem-solving and decision-making abilities; Strong communication skills and the ability to lead cross-functional teams; Passion for mentoring and developing engineers.

As a Business Analyst & Data Annotator, you will play a crucial role in gathering and analyzing business requirements, acting as a bridge between stakeholder needs and technical teams. You will also handle the data annotation process, ensuring the production of high-quality, accurately labeled datasets necessary for training machine learning models. This role involves close collaboration with ML engineers, data scientists, and business teams to ensure that data aligns with project goals. Your work will center on translating complex business needs and technical specifications into clear instructions, managing data labeling workflows, and maintaining data quality standards. A junior-level candidate with strong English skills (B2 or higher, ideally C1), attention to detail, and a good understanding of business and technical concepts can be successful in this role, especially when working with reports containing specialized terminology. Responsibilities: Develop and implement detailed guidelines and instructions for data labeling and annotation to ensure consistency and accuracy across datasets; Review and validate labeled data, providing constructive feedback to annotation teams to improve data quality and adherence to project standards; Collaborate with data scientists and ML engineers to prepare, organize, and support the creation of high-quality annotated datasets for model training; Manage the annotation workflow, prioritize tasks, and track progress to ensure timely delivery of labeled data; Maintain high standards of data privacy, security, and compliance throughout all annotation processes; Gather and analyze business requirements, workflows, and terminology to understand data needs and improve annotation processes; Facilitate communication between technical teams and stakeholders by translating complex technical or domain-specific language into clear, accessible instructions and explanations; Offer insights into business processes that could benefit from automation or ML solutions, supporting the design and implementation of such projects; Support continuous improvement of data annotation guidelines, workflows, and overall business analysis practices to enhance efficiency and data quality. Requirements: At least 1 year of experience in the relevant role; Excellent English language skills (B2 level or higher, ideally C1), especially when working with reports containing complex terminology; Strong analytical skills and an understanding of business workflows; Attention to detail and ability to create clear instructions and guidelines for annotation teams; Understanding of data privacy, security standards, and compliance requirements. Nice to Have: Basic knowledge of machine learning concepts and data management principles; Familiarity with ML workflows, data pipelines, and MLOps tools; Experience with cloud platforms such as AWS, GCP, or Azure; Experience with data labeling or annotation; Experience in creating markups for AI; Insurance industry background.

As a Business Analyst & Data Annotator, you will play a crucial role in gathering and analyzing business requirements, acting as a bridge between stakeholder needs and technical teams. You will also handle the data annotation process, ensuring the production of high-quality, accurately labeled datasets necessary for training machine learning models. This role involves close collaboration with ML engineers, data scientists, and business teams to ensure that data aligns with project goals. Your work will center on translating complex business needs and technical specifications into clear instructions, managing data labeling workflows, and maintaining data quality standards. Responsibilities: Develop and implement detailed guidelines and instructions for data labeling and annotation to ensure consistency and accuracy across datasets; Review and validate labeled data, providing constructive feedback to annotation teams to improve data quality and adherence to project standards; Collaborate with data scientists and ML engineers to prepare, organize, and support the creation of high-quality annotated datasets for model training; Manage the annotation workflow, prioritize tasks, and track progress to ensure timely delivery of labeled data; Maintain high standards of data privacy, security, and compliance throughout all annotation processes; Gather and analyze business requirements, workflows, and terminology to understand data needs and improve annotation processes; Facilitate communication between technical teams and stakeholders by translating complex technical or domain-specific language into clear, accessible instructions and explanations; Offer insights into business processes that could benefit from automation or ML solutions, supporting the design and implementation of such projects; Support continuous improvement of data annotation guidelines, workflows, and overall business analysis practices to enhance efficiency and data quality. Requirements: At least 1 year of experience in the relevant role; Excellent English language skills (B2 level or higher, ideally C1), especially when working with reports containing complex terminology; Strong analytical skills and an understanding of business workflows; Attention to detail and ability to create clear instructions and guidelines for annotation teams; Understanding of data privacy, security standards, and compliance requirements. Nice to Have: Basic knowledge of machine learning concepts and data management principles; Familiarity with ML workflows, data pipelines, and MLOps tools; Experience with cloud platforms such as AWS, GCP, or Azure; Experience with data labeling or annotation; Experience in creating markups for AI; Insurance industry background.

Provectus helps companies adopt ML/AI to transform the ways they operate, compete, and drive value. The focus of the company is on building ML Infrastructure to drive end-to-end AI transformations, assisting businesses in adopting the right AI use cases, and scaling their AI initiatives organization-wide in such industries as Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses. We are seeking a highly skilled Machine Learning (ML) Tech Lead with a strong background in Large Language Models (LLMs) and AWS Cloud services. The ideal candidate will oversee the development and deployment of cutting-edge AI solutions while managing a team of 5-10 engineers. This leadership role demands hands-on technical expertise, strategic planning, and team management capabilities to deliver innovative products at scale. Responsibilities: Leadership & Management Lead and manage a team of 5-10 engineers, providing mentorship and fostering a collaborative team environment; Drive the roadmap for machine learning projects aligned with business goals; Coordinate cross-functional efforts with product, data, and engineering teams to ensure seamless delivery. Machine Learning & LLM Expertise Design, develop, and fine-tune LLMs and other machine learning models to solve business problems; Evaluate and implement state-of-the-art LLM techniques for NLP tasks such as text generation, summarization, and entity extraction; Stay ahead of advancements in LLMs and apply emerging technologies; Expertise in multiple main fields of ML: NLP, Computer Vision, RL, deep learning and classical ML. AWS Cloud Expertise Architect and manage scalable ML solutions using AWS services (e.g., SageMaker, Lambda, Bedrock, S3, ECS, ECR, etc.); Optimize models and data pipelines for performance, scalability, and cost-efficiency in AWS; Ensure best practices in security, monitoring, and compliance within the cloud infrastructure. Technical Execution Oversee the entire ML lifecycle, from research and experimentation to production and maintenance; Implement MLOps and LLMOps practices to streamline model deployment and CI/CD workflows; Debug, troubleshoot, and optimize production ML models for performance. Team Development & Communication Conduct regular code reviews and ensure engineering standards are upheld; Facilitate professional growth and learning for the team through continuous feedback and guidance; Communicate progress, challenges, and solutions to stakeholders and senior leadership. Qualifications: Proven experience with LLMs and NLP frameworks (e.g., Hugging Face, OpenAI, or Anthropic models); Strong expertise in AWS Cloud Services; Strong experience in ML/AI, including at least 2 years in a leadership role; Hands-on experience with Python, TensorFlow/PyTorch, and model optimization; Familiarity with MLOps tools and best practices; Excellent problem-solving and decision-making abilities; Strong communication skills and the ability to lead cross-functional teams; Passion for mentoring and developing engineers.

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.

Join us at Provectus as part of a team 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. We are seeking a talented and versatile Full-Stack Engineer with strong experience in Next.js to join our innovative team. Requirements: 5+ years of experience in front-end development; Production experience with Next.js v13+ (App Router, Server Actions); Experience with Nest.js; Solid knowledge of TypeScript; Solid understanding of relational and non-relational databases (Aurora, DynamoDB); Familiarity with ChakraUI or similar UI Component Libraries; Proficiency in writing unit and integration tests; Full application lifecycle experience; Excellent communication and collaboration skills; Understanding of design patterns and software architectures; Experience with cloud services, preferably AWS Cloud; Proficiency with GIT for version control. Preferred Qualifications: Experience with AWS services; Experience setting up CI/CD pipelines; Understanding of testing principles and experience with testing tools. Responsibilities: Write well-designed, testable, efficient code by using best software development practices; Work closely, collaboratively, and creatively with product owners to build a user experience to support the business users' needs; Work well in a team environment of 4 - 5 developers, but taking individual ownership of deliverables and ensuring quality through comprehensive unit tests; Code optimization and performance with best practices in Javascript; Create a user interface by using standard HTML/CSS practices; Gather and refine specifications and requirements based on technical needs; Ensure cross-browser capability of code and HTML markup; Create and maintain software documentation; Stay tuned with the recent Web technologies.

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.

Provectus helps companies adopt ML/AI to transform the ways they operate, compete, and drive value. The focus of the company is on building ML Infrastructure to drive end-to-end AI transformations, assisting businesses in adopting the right AI use cases, and scaling their AI initiatives organization-wide in such industries as Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses. We are seeking a highly skilled Machine Learning (ML) Tech Lead with a strong background in Large Language Models (LLMs) and AWS Cloud services. The ideal candidate will oversee the development and deployment of cutting-edge AI solutions while managing a team of 5-10 engineers. This leadership role demands hands-on technical expertise, strategic planning, and team management capabilities to deliver innovative products at scale. Responsibilities: Leadership & Management Lead and manage a team of 5-10 engineers, providing mentorship and fostering a collaborative team environment; Drive the roadmap for machine learning projects aligned with business goals; Coordinate cross-functional efforts with product, data, and engineering teams to ensure seamless delivery. Machine Learning & LLM Expertise Design, develop, and fine-tune LLMs and other machine learning models to solve business problems; Evaluate and implement state-of-the-art LLM techniques for NLP tasks such as text generation, summarization, and entity extraction; Stay ahead of advancements in LLMs and apply emerging technologies; Expertise in multiple main fields of ML: NLP, Computer Vision, RL, deep learning and classical ML. AWS Cloud Expertise Architect and manage scalable ML solutions using AWS services (e.g., SageMaker, Lambda, Bedrock, S3, ECS, ECR, etc.); Optimize models and data pipelines for performance, scalability, and cost-efficiency in AWS; Ensure best practices in security, monitoring, and compliance within the cloud infrastructure. Technical Execution Oversee the entire ML lifecycle, from research and experimentation to production and maintenance; Implement MLOps and LLMOps practices to streamline model deployment and CI/CD workflows; Debug, troubleshoot, and optimize production ML models for performance. Team Development & Communication Conduct regular code reviews and ensure engineering standards are upheld; Facilitate professional growth and learning for the team through continuous feedback and guidance; Communicate progress, challenges, and solutions to stakeholders and senior leadership. Qualifications: Proven experience with LLMs and NLP frameworks (e.g., Hugging Face, OpenAI, or Anthropic models); Strong expertise in AWS Cloud Services; Strong experience in ML/AI, including at least 2 years in a leadership role; Hands-on experience with Python, TensorFlow/PyTorch, and model optimization; Familiarity with MLOps tools and best practices; Excellent problem-solving and decision-making abilities; Strong communication skills and the ability to lead cross-functional teams; Passion for mentoring and developing engineers.

Provectus helps companies adopt ML/AI to transform the ways they operate, compete, and drive value. The focus of the company is on building ML Infrastructure to drive end-to-end AI transformations, assisting businesses in adopting the right AI use cases, and scaling their AI initiatives organization-wide in such industries as Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses. We are seeking a highly skilled Machine Learning (ML) Tech Lead with a strong background in Large Language Models (LLMs) and AWS Cloud services. The ideal candidate will oversee the development and deployment of cutting-edge AI solutions while managing a team of 5-10 engineers. This leadership role demands hands-on technical expertise, strategic planning, and team management capabilities to deliver innovative products at scale. Responsibilities: Leadership & Management Lead and manage a team of 5-10 engineers, providing mentorship and fostering a collaborative team environment; Drive the roadmap for machine learning projects aligned with business goals; Coordinate cross-functional efforts with product, data, and engineering teams to ensure seamless delivery. Machine Learning & LLM Expertise Design, develop, and fine-tune LLMs and other machine learning models to solve business problems; Evaluate and implement state-of-the-art LLM techniques for NLP tasks such as text generation, summarization, and entity extraction; Stay ahead of advancements in LLMs and apply emerging technologies; Expertise in multiple main fields of ML: NLP, Computer Vision, RL, deep learning and classical ML. AWS Cloud Expertise Architect and manage scalable ML solutions using AWS services (e.g., SageMaker, Lambda, Bedrock, S3, ECS, ECR, etc.); Optimize models and data pipelines for performance, scalability, and cost-efficiency in AWS; Ensure best practices in security, monitoring, and compliance within the cloud infrastructure. Technical Execution Oversee the entire ML lifecycle, from research and experimentation to production and maintenance; Implement MLOps and LLMOps practices to streamline model deployment and CI/CD workflows; Debug, troubleshoot, and optimize production ML models for performance. Team Development & Communication Conduct regular code reviews and ensure engineering standards are upheld; Facilitate professional growth and learning for the team through continuous feedback and guidance; Communicate progress, challenges, and solutions to stakeholders and senior leadership. Qualifications: Proven experience with LLMs and NLP frameworks (e.g., Hugging Face, OpenAI, or Anthropic models); Strong expertise in AWS Cloud Services; Strong experience in ML/AI, including at least 2 years in a leadership role; Hands-on experience with Python, TensorFlow/PyTorch, and model optimization; Familiarity with MLOps tools and best practices; Excellent problem-solving and decision-making abilities; Strong communication skills and the ability to lead cross-functional teams; Passion for mentoring and developing engineers; Familiarity with Amazon Bedrock would be considered a significant plus.