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Remote Rag Jobs in Colorado (NOW HIRING)

Eliassen Group

Senior AI/ML Engineer

Almont, CO · Remote

$90 - $100/hr

Remote Our client seeks a Senior AI/ML Engineer to design and deliver cloud-native machine learning solutions on AWS. The role includes LLM orchestration, RAG pipelines, vector database integration ...

Sovrn

Principal Software Engineer

Boulder, CO · On-site +1

$200K - $240K/yr

Establish and champion AI engineering practices across the team - from prompt engineering and RAG ... For exceptional candidates we would consider remote locations in these states: AZ, CA, CO, GA, MD ...

The Mutual Group

Lead AI Engineer - AWS Platform

Denver, CO · On-site +1

$130K - $190K/yr

Build RAG pipelines using vector databases and enterprise data sources * Build machine learning ... Flexible work schedules and hybrid/remote options for eligible positions * Educational assistance ...

DSC Resources

Principal Data Engineering

Louisville, CO · Remote

$180K - $205K/yr

Remote Principal Software Engineer - Data Architecture and Engineering US-based only | No visa ... vector databases, embeddings, RAG patterns) · Lead zero-downtime data migrations and ...

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What are the key skills and qualifications needed to thrive as a Remote Rag, and why are they important?

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What are some common challenges faced by professionals working in a remote RAG (Responsible AI Governance) role?

Professionals in remote RAG roles often encounter challenges related to cross-functional collaboration and maintaining clear communication, especially when working across different time zones. Ensuring alignment on ethical AI standards and compliance requirements can be complex, as it typically involves coordinating with data scientists, legal teams, and business stakeholders. Staying current with evolving regulatory frameworks and best practices in AI governance is also essential, demanding continuous learning and adaptability. Building trust and rapport within a remote team can require extra effort, but leveraging digital collaboration tools and regular check-ins can help mitigate these challenges.

What is a Remote RAG (Retrieval-Augmented Generation) specialist?

A Remote RAG specialist is a professional who works with Retrieval-Augmented Generation (RAG) systems, typically in the field of artificial intelligence and machine learning. RAG combines traditional information retrieval techniques with generative models like large language models to provide more accurate and contextually relevant answers to user queries. Remote RAG specialists often build, fine-tune, and maintain these systems while working from a remote location. They may also work on integrating RAG models into applications, improving retrieval accuracy, and customizing outputs based on user needs.
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Remote Rag jobs near you

Machine Learning Engineer

Judi Health

Denver, CO • Remote

Other

Posted 22 days ago

Job description

Position Summary: 

Join our mission to infuse cutting-edge AI/ML/GenAI into pharmacy benefits as a Machine Learning Engineer.  We are looking for a software engineer with machine learning expertise to join us in expanding our AI capabilities, enabling increased productivity and magical experiences in our products and services. 

In this role you will be expected to design and implement complex AI systems that leverage ML models for NLP, NLG, multimodal data analysis, chatbots, and RAG-based QnA. The ideal candidate should be passionate about applying AI/ML concepts to difficult problems and develop scalable solutions. We want people who like working in a collaborative team environment and enjoy creating practical, efficient, and high-performance software that leverages Large Language Models (LLM), Multimodal Language Models(MLM), and other ML models and techniques to build amazing capabilities for our customers, partners, and employees. Most importantly, we are a mission-oriented, high-growth startup and we are looking for folks that are excited to be part of our journey to make lasting impact towards transforming healthcare. 

Responsibilities: 

  • Develop and productionize machine learning (ML) solutions in the fields of Document understanding, Search and QnA, GenAI, Virtual Agents, etc. 
  • Develop and maintain backend services using Python, focusing on AI-driven applications. 
  • Design and implement APIs for seamless integration with AI models and services. 
  • Develop tools for large-scale data processing and ETL and contribute to extracting insights from data to help guide ML systems development 
  • Participate in code reviews, testing, and quality assurance processes. 
  • Troubleshoot and resolve technical issues related to AI model, integration, deployment and backend services. 
  • Develop algorithms to ensure the integrity and robustness of ML solutions by developing automated testing and validation processes. 
  • Document and communicate development processes and implementation details with peers and supervisors 
  • Ensure the security and compliance of healthcare data, adhering to HIPAA regulations. 

Required Qualifications: 

  • Bachelor's or Master's degree in Computer Science, Machine Learning, or a related quantitative field. 
  • 2+ years experience in the industry with a strong focus on ML solutions development and production deployment is a plus. 
  • Good understanding of Machine Learning fundamentals such as Deep Neural Networks, Transformer-based LLM and MLMs, Boosted or standard decision trees, RAG, etc.  
  • Strong grasp of OOP, Design Patterns, efficient algorithms, and quality software development. 
  • Proficiency in Python and familiarity with ML libraries such as PyTorch. 
  • Familiarity with GenAI services such as OpenAI GPT, Anthropic Claude, Google Gemma etc. Some experience in developing and fine-tuning prompts on any of the GenAI services is a plus. 
  • Excellent problem-solving skills, attention to detail, and a strong capacity for logical thinking. 
  • The ability to work collaboratively across multiple disciplines in an extremely fast-paced, startup environment. 
  • Good written communication skills that enable collaboration in a remote environment. 

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