Provectus

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.

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 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 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 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.

A Practice Leader is the first point of contact for their direct reports, and they liaise and relay information between senior leaders, HR, and engineers. 

The role of a Practice Leader is probably the most important because this is the first line of people management, and it provides things to be done. On the one hand, a PL is a part of the Practice, so this person must be at least senior-level in their specialty. On the other hand, a Practice Leader is a people manager.

The complete list of requirements looks like the following:
- Practical experience and strong understanding of Python patterns & best practices.
- Strong understanding of ML project lifecycle.
- Practical experience with creating training datasets involving human annotators.
- Experience with writing Deep Learning models from scratch.
- Experience in >1 of the following areas: NLP, CV, forecasting, recommender systems.
- Strong experience with agentic workflows, RAG architecture, and GraphRAG.
- Experience and in-depth understanding of transformers.
- Practical experience with /AWS/other cloud/open source alternatives/ MLOps platforms, frameworks, and libraries.
- Practical experience with model post-production & maintenance: model and data monitoring, retraining automation, etc.
- Ability to make reusable components of ML pipelines.
- Practical experience with a variety of data sources (OLTP, OLAP, DataLake, Streaming).
- Experience in DataOps or ML/MLOps would be a significant plus.
- Ability to explain decisions, status, and roadmap to non-technical customer representatives.
- Experience in team/department leadership.
- Ability to teach and mentor. The role assumes providing employees with their career path and helping them achieve goals.
- Diplomatic skills. It means more than just "communication skills" and includes ethics, empathy, compassion, and the ability to resolve conflicts.
- Calmness. People are complicated, and you need to be ready for any objectives or misunderstandings.
 

Responsibilities:
- Build effective teams.
- Participate in meetups, conferences, and build community.
- Share best practices and culture with the team.
- Mentor engineers, coach Team Leads, and encourage others to share knowledge.
- Have technical excellence and be an influencer in different teams/projects.
- Hire and onboard newcomers.
- Conduct performance reviews, 1-on-1 meetings.
- Identify and address team gaps in knowledge.
- Evaluate, improve, and maintain processes.
- Сollaborate with other managers across the company.
- Communicate and follow the company's mission, vision, and values.

A Practice Leader is the first point of contact for their direct reports, and they liaise and relay information between senior leaders, HR, and engineers. 

The role of a Practice Leader is probably the most important because this is the first line of people management, and it provides things to be done. On the one hand, a PL is a part of the Practice, so this person must be at least senior-level in their specialty. On the other hand, a Practice Leader is a people manager.

The complete list of requirements looks like the following:
- Practical experience and strong understanding of Python patterns & best practices.
- Strong understanding of ML project lifecycle.
- Practical experience with creating training datasets involving human annotators.
- Experience with writing Deep Learning models from scratch.
- Experience in >1 of the following areas: NLP, CV, forecasting, recommender systems.
- Strong experience with agentic workflows, RAG architecture, and GraphRAG.
- Experience and in-depth understanding of transformers.
- Practical experience with /AWS/other cloud/open source alternatives/ MLOps platforms, frameworks, and libraries.
- Practical experience with model post-production & maintenance: model and data monitoring, retraining automation, etc.
- Ability to make reusable components of ML pipelines.
- Practical experience with a variety of data sources (OLTP, OLAP, DataLake, Streaming).
- Experience in DataOps or ML/MLOps would be a significant plus.
- Ability to explain decisions, status, and roadmap to non-technical customer representatives.
- Experience in team/department leadership.
- Ability to teach and mentor. The role assumes providing employees with their career path and helping them achieve goals.
- Diplomatic skills. It means more than just "communication skills" and includes ethics, empathy, compassion, and the ability to resolve conflicts.
- Calmness. People are complicated, and you need to be ready for any objectives or misunderstandings.
 

Responsibilities:
- Build effective teams.
- Participate in meetups, conferences, and build community.
- Share best practices and culture with the team.
- Mentor engineers, coach Team Leads, and encourage others to share knowledge.
- Have technical excellence and be an influencer in different teams/projects.
- Hire and onboard newcomers.
- Conduct performance reviews, 1-on-1 meetings.
- Identify and address team gaps in knowledge.
- Evaluate, improve, and maintain processes.
- Сollaborate with other managers across the company.
- Communicate and follow the company's mission, vision, and values.