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Mlops Data Engineer Jobs in Utah (NOW HIRING)

Waystar

Senior ML Engineer

Lehi, UT · On-site

$98K - $134K/yr

Data Pipeline & Knowledge Base Construction: * Design, implement, and optimize robust pipelines for ... MLOps & Deployment: * Deploy, manage, and monitor LMs and agentic components on Google Cloud ...

Berkadia

Senior Engineer - Machine Learning

Midvale, UT · Hybrid

$98K - $135K/yr

You will work closely with product, engineering, architecture, and data teams to design, implement ... Establish and contribute to best practices in MLOps, including model deployment, monitoring ...

Berkadia

Senior Engineer - Machine Learning

Midvale, UT · On-site

$98K - $135K/yr

You will work closely with product, engineering, architecture, and data teams to design, implement ... Establish and contribute to best practices in MLOps, including model deployment, monitoring ...

Leash Bio

Machine Learning Engineer

Salt Lake City, UT · On-site

Responsibilities : • Manage and optimize data processing workflows for large-scale datasets, with ... Preferred : • Familiarity with common MLops tooling (e.g., Dagster, Prefect, Airflow, Docker ...

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Mlops Data Engineer information

What is an MLOps data engineer?

MLOps Data Engineers are professionals who blend expertise in machine learning (ML), operations (Ops), and data engineering to streamline the deployment and management of ML models in production environments. They design and maintain data pipelines, automate workflows, and ensure the scalability, reliability, and reproducibility of machine learning systems. Their role bridges the gap between data scientists and IT operations, enabling seamless integration of ML models into real-world applications.

What are the key skills and qualifications needed to thrive as an MLOps data engineer?

To thrive as an MLOps Data Engineer, you need a strong background in data engineering, machine learning workflows, and software development, usually supported by a degree in computer science or a related field. Expertise with cloud platforms (such as AWS, GCP, or Azure), CI/CD pipelines, containerization tools (like Docker and Kubernetes), and familiarity with orchestration frameworks are typically required, along with certifications in cloud or data engineering. Strong problem-solving abilities, collaboration, and clear communication set professionals apart in this role. These skills and qualities are critical to efficiently deploying scalable machine learning solutions and ensuring smooth collaboration between data science and engineering teams.

What are some common challenges MLOps data engineers face when deploying machine learning models into production?

MLOps Data Engineers often encounter challenges such as ensuring seamless integration between data pipelines and model serving infrastructure, managing consistent data quality, and automating model retraining and monitoring. Another common hurdle is maintaining scalability and reliability as data volumes grow, and efficiently collaborating with data scientists, software engineers, and DevOps teams. Addressing these challenges requires strong communication skills, familiarity with cloud platforms, and a proactive approach to troubleshooting and automation.

What is the difference between Mlops Data Engineer vs Data Scientist?

AspectMlops Data EngineerData Scientist
Required SkillsMachine learning deployment, cloud platforms, scripting, data pipelinesStatistical analysis, programming, data visualization, machine learning modeling
CertificationsCloud certifications, ML engineering coursesData science certifications, statistical courses
Work EnvironmentData pipelines, cloud infrastructure, ML deployment systemsData analysis, modeling, research environments
Industry UsageTech companies, AI-focused firms, cloud service providersResearch institutions, analytics firms, tech companies

The main difference between an Mlops Data Engineer and a Data Scientist lies in their focus areas. Mlops Data Engineers specialize in deploying, maintaining, and scaling machine learning models within production environments, emphasizing infrastructure and automation. Data Scientists primarily focus on analyzing data, building models, and deriving insights. Both roles require strong technical skills, but their day-to-day tasks and career paths differ significantly.

Are MLOps Data Engineers in demand?

MLOps Data Engineers are in high demand due to the increasing adoption of machine learning and AI across industries. They are skilled in deploying, managing, and maintaining ML models using tools like Docker, Kubernetes, and cloud platforms, making their expertise highly sought after in data-driven organizations.

Is MLOps required for data engineers?

MLOps is increasingly important for data engineers involved in deploying and maintaining machine learning models, as it encompasses practices like automation, monitoring, and version control. While not always mandatory, knowledge of MLOps tools such as Docker, Kubernetes, and CI/CD pipelines enhances a data engineer's ability to support scalable and reliable ML systems.

What are popular job titles related to Mlops Data Engineer jobs in Utah?

For Mlops Data Engineer jobs in Utah, the most frequently searched job titles are:

What job categories do people searching Mlops Data Engineer jobs in Utah look for?

The top searched job categories for Mlops Data Engineer jobs in Utah are:

What cities in Utah are hiring for Mlops Data Engineer jobs?

Cities in Utah with the most Mlops Data Engineer job openings:

Mlops Data Engineer jobs near you

Senior ML Engineer

Waystar

Lehi, UT • On-site

$98K - $134K/yr

Full-time

Medical, Retirement, PTO

Re-posted 17 days ago

Job description

ABOUT THIS POSITION
Job Description Summary
We are seeking a highly skilled and innovative Machine Learning Engineer with a passion for building robust, efficient, and domain-specific AI systems using Language Models (LMs) and agentic architectures. As a core member of the team, you will be instrumental in developing the entire ML pipeline, from sophisticated data extraction techniques to fine-tuning specialized LMs and orchestrating their interactions within a multi-agent framework.
This is a unique opportunity to apply state-of-the-art Generative AI and NLP techniques to a real-world, high-impact problem, leveraging the latest research in agentic AI and LMs to deliver economical and powerful solutions.
WHAT YOU'LL DO
    • Data Pipeline & Knowledge Base Construction:

    • Design, implement, and optimize robust pipelines for ingesting, parsing, and extracting structured information from complex documents (leveraging OCR, document layout analysis, Named Entity Recognition (NER), and Relationship Extraction (RE)).

    • Develop rich, nested JSON schemas for representing structured data and ensure scalable storage

    • Generate and manage high-quality vector embeddings for efficient retrieval-augmented generation (RAG) within a Vector Database.

    • Language Model (LM) Development & Fine-tuning:

    • Research, select, and experiment with appropriate open-source Language Models (Large & Small) (e.g., Phi-3, Mistral, Llama, Nemotron-H families) for specialized tasks.

    • Design and execute efficient fine-tuning strategies (e.g., LoRA, QLoRA, full fine-tuning) on curated, domain-specific datasets to achieve precise performance for tasks like coverage determination, code lookups, and policy rule application.

    • Explore and implement knowledge distillation techniques to transfer capabilities from larger models to smaller, more efficient LMs.

    • Agentic System Design & Implementation:

    • Build and maintain the core agentic framework, including the orchestrator that intelligently routes queries and coordinates interactions between various specialized LM tools.

    • Develop and integrate "tools" (specialized LMs and external APIs) that perform atomic medical necessity tasks, ensuring strict behavioral alignment and structured outputs.

    • MLOps & Deployment:

    • Deploy, manage, and monitor LMs and agentic components on Google Cloud Platform (GCP) using services like Vertex AI, GKE, Cloud Functions, and Cloud Run.

    • Implement robust MLOps practices for continuous integration, continuous delivery (CI/CD), model versioning, and performance monitoring (latency, throughput, accuracy).

    • Continuous Improvement & Research:

    • Establish effective feedback loops from end-user interactions and system logs to identify areas for model improvement.

    • Curate and expand training datasets, ensuring data privacy (PHI/PII masking) and legal compliance.

    • Stay abreast of the latest research in LMs, agentic AI, NLP, and document understanding, applying relevant advancements to our system.

    • Collaboration:

    • Work closely with subject matter experts, product managers, and other engineers to translate complex requirements into technical solutions and evaluate system performance.

    • Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field.

    • 3+ years of professional experience in Machine Learning Engineering, with a strong focus on NLP.

    • Proven experience with Language Models (LMs), including model selection, fine-tuning, and deployment.

    • Strong proficiency in Python and familiarity with ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face Transformers).

    • Solid understanding and hands-on experience with core NLP techniques and architectures, especially Transformers.

    • Experience with cloud platforms, particularly Google Cloud Platform (GCP), including services like Vertex AI, Cloud Storage, and compute services.

    • Familiarity with MLOps principles and tools for model serving, monitoring, and pipeline automation.

    • Excellent problem-solving skills, attention to detail, and ability to work independently and collaboratively.

    • Active use of artificial intelligence (AI) tools and techniques to enhance performance, drive innovation, and improve decision-making across business functions.

    • Ability to leverage AI tools and platforms to streamline workflows, improve decision-making, and drive innovation.

    • Curiosity and adaptability in exploring emerging AI technologies, with a mindset for continuous learning and experimentation.

WHAT YOU'LL NEED
  • What Will Make You Stand Out (Preferred Qualifications):
    • Hands-on experience building or contributing to agentic AI systems or multi-agent frameworks.

    • Direct experience with document processing technologies such as OCR, layout parsing, Document AI, or custom information extraction from unstructured text.

    • Experience with Vector Databases (e.g., pgvector, Pinecone, Weaviate, Qdrant) and RAG architectures.

    • Exposure to the healthcare domain, particularly understanding medical terminology, CPT/ICD codes, or regulatory documents.

ABOUT WAYSTAR
Through a smart platform and better experience, Waystar helps providers simplify healthcare payments and yield powerful results throughout the complete revenue cycle.
Waystar's healthcare payments platform combines innovative, cloud-based technology, robust data, and unparalleled client support to streamline workflows and improve financials so providers can focus on what matters most: their patients and communities. Waystar is trusted by 1M+ providers, 1K+ hospitals and health systems, and is connected to over 5K commercial and Medicaid/Medicare payers. We are deeply committed to living out our organizational values: honesty; kindness; passion; curiosity; fanatical focus; best work, always; making it happen; and joyful, optimistic & fun.
Waystar products have won multiple Best in KLAS® or Category Leader awards since 2010 and earned multiple #1 rankings from Black Book™ surveys since 2012. The Waystar platform supports more than 500,000 providers, 1,000 health systems and hospitals, and 5,000 payers and health plans. For more information, visit waystar.com or follow @Waystar on Twitter.
WAYSTAR PERKS
  • Competitive total rewards (base salary + bonus, if applicable)
  • Customizable benefits package (3 medical plans with Health Saving Account company match)
  • We offer generous paid time off for our non-exempt team members, starting with 3 weeks + 13 paid holidays, including 2 personal floating holidays. We also offer flexible time off for our exempt team members + 13 paid holidays
  • Paid parental leave (including maternity + paternity leave)
  • Education assistance opportunities and free LinkedIn Learning access
  • Free mental health and family planning programs, including adoption assistance and fertility support
  • 401(K) program with company match
  • Pet insurance
  • Employee resource groups

Waystar is proud to be an equal opportunity workplace. We celebrate, value, and support diversity and inclusion. Qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, national origin, disability status, genetics, marital status, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.
This applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

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