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

To thrive as a Remote MLOps Engineer, you need a strong background in machine learning, software engineering, and cloud computing, typically supported by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, GCP, Azure), and experience with ML frameworks such as TensorFlow or PyTorch are crucial, along with relevant certifications. Excellent communication, problem-solving abilities, and self-motivation are essential soft skills for collaborating across distributed teams and handling complex deployments. These skills ensure the seamless integration, deployment, and monitoring of machine learning models in production environments, driving efficiency and reliability in remote settings.

What are some common challenges faced by remote MLOps engineers, and how can they be overcome?

Remote MLOps engineers often face challenges related to collaborating across distributed teams, ensuring robust CI/CD pipelines for machine learning models, and maintaining secure, scalable cloud infrastructure. Effective communication using collaboration tools and thorough documentation is key to overcoming team coordination issues. Additionally, leveraging cloud-based MLOps platforms and automating routine processes can help streamline workflows and reduce operational friction, allowing engineers to focus on innovation and model optimization.

What is a Remote MLOps job?

A Remote MLOps job involves managing and automating the deployment, monitoring, and maintenance of machine learning models in production environments, all while working from a remote location. MLOps stands for Machine Learning Operations, and professionals in this role bridge the gap between data science and IT operations to ensure smooth, reliable model performance. Remote MLOps engineers use tools and practices to streamline machine learning workflows, collaborate with distributed teams, and maintain infrastructure without being tied to a physical office.

What is the difference between Remote Mlops vs Data Engineer?

AspectRemote MlopsData Engineer
Required CredentialsCertifications in cloud platforms, ML frameworks, scripting skillsDatabase, ETL, SQL, cloud certifications
Work EnvironmentRemote, cloud-based, collaboration with ML teamsRemote or on-site, data infrastructure focus
Industry UsageAI/ML companies, tech firms, startupsData-driven companies, finance, healthcare, tech
Common Search/ComparisonYesYes

Remote Mlops and Data Engineers share overlapping skills like cloud computing and scripting, but Remote Mlops focuses on deploying and maintaining ML models in production, while Data Engineers build and manage data pipelines. Both roles are essential in data-driven organizations, often collaborating but with distinct technical focuses.

What are the most commonly searched types of Mlops jobs in Georgia? The most popular types of Mlops jobs in Georgia are:
What are popular job titles related to Remote Mlops jobs in Georgia? For Remote Mlops jobs in Georgia, the most frequently searched job titles are:
What job categories do people searching Remote Mlops jobs in Georgia look for? The top searched job categories for Remote Mlops jobs in Georgia are:
What cities in Georgia are hiring for Remote Mlops jobs? Cities in Georgia with the most Remote Mlops job openings:
Remote Mlops jobs near you
Corporate Director, Data Science & AI Engineering

Corporate Director, Data Science & AI Engineering

Emory Healthcare

Atlanta, GA • On-site, Remote

Full-time

Posted 11 days ago

Emory Healthcare rating

7.7

Company rating: 7.7 out of 10

Based on 207 frontline employees who took The Breakroom Quiz

158th of 864 rated healthcare providers

Job description

Be inspired.  Be rewarded. Belong. At Emory Healthcare. 

At Emory Healthcare we fuel your professional journey with better benefits, valuable resources, ongoing mentorship and leadership programs for all types of jobs, and a supportive environment that enables you to reach new heights in your career and be what you want to be.  We provide: 

  • Comprehensive health benefits that start day 1 
  • Student Loan Repayment Assistance & Reimbursement Programs 
  • Family-focused benefits  
  • Wellness incentives 
  • Ongoing mentorship, development, and leadership programs  
  • And more  

Ideally seeking an Atlanta based candidate able to visit our Atlanta based office, but may consider remote options in the following locations:  applicants residing in or able to relocate to the following states are eligible for hire: Alabama, Arkansas, Florida, Georgia, Illinois, Louisiana, Michigan, New Hampshire, North Carolina, Ohio, Pennsylvania, South Carolina, Tennessee, Texas, Virginia, and Wisconsin


Emory Healthcare (EHC) — Georgia's most comprehensive academic health system — is investing boldly in its AI-powered data future. As we modernize our enterprise data platform on Microsoft Fabric and scale AI capabilities across clinical, research, and operational domains, we are searching for an exceptional leader to serve as Corporate Director, Data Science & AI Engineering.
Dual reporting to the Chief Data & Analytics Officer (CDAO) and the Chief AI Officer (CAIO), this role is the connective tissue between enterprise strategy and AI/ML execution. You will own the data science management vision, lead a multidisciplinary team that includes AI Engineers and Data Scientists, and serve as the primary architect of how Emory Healthcare harnesses advanced machine learning, generative AI, and large-scale analytics to improve patient outcomes and institutional performance.
This is not a theoretical strategy role. We expect our Director to be equally fluent in designing data Science and AI  frameworks, deploying ML models into production, overseeing LLMOps pipelines, and translating complex analytical findings into boardroom-ready strategy — all within a dynamic, mission-driven health system environment.
 
RESPONSIBILITIES:
 
Below is a comprehensive list of the areas this person will be leading:
AI & Machine Learning Operations

  • Provide strategic direction for the design, deployment, and lifecycle management of ML and AI models across clinical and operational use cases.

  • Establish and mature MLOps and LLMOps practices — including model versioning, monitoring, drift detection, and responsible AI guardrails — in collaboration with AI Engineers.

  • Champion the integration of Generative AI and large language model (LLM) capabilities into EHC workflows, identifying high-value use cases and ensuring safe, governed deployment.
  • Partner with the CDAO and CAIO Offices and Emory Digital/OIT to build a scalable, cloud-native ML infrastructure on Microsoft Fabric and Azure, enabling rapid experimentation and production-grade AI delivery.
Data & Advanced Analytics Strategy

  • Collaborate with the Corp Director AI Strategy, Corp Director Data Engineering, and Data & AI Governance Manager to obtain a deep understanding of stakeholder data needs across the Health System and translate those needs into a cohesive, institution-wide executable Data & AI Models.

  • Catalog existing data shortcomings, establish common definitions, and lead initiatives to reduce reporting redundancies and increase data access, sharing, and consumption.

  • Drive advanced analytics initiatives — including predictive modeling, NLP, and population health analytics — that directly inform strategic fundraising, clinical operations, and resource planning.

  • Proactively mine all data sources for untapped opportunities; surface patterns through data modeling that enhance EHC's Digital Data & Analytics roadmap.

Data and AI Governance & Management

  • Design and implement comprehensive data/AI governance policies, data quality frameworks, and data standards in collaboration with Emory partners.

  • Own data lifecycle management strategy: ingestion, transformation, quality, archiving, and retention across structured and unstructured datasets.

  • Work with legal, compliance, and IT to ensure data privacy (HIPAA, GDPR), ethical AI use, and responsible data stewardship practices are embedded in all programs.

  • Lead the modernization of shared data management and analytics architecture, facilitating joint collaborations that leverage Fabric-based shared infrastructure and resources.

Team Leadership & Cross-Functional Collaboration

  • Recruit, develop, and lead a high-performing team of AI Engineers and Data Scientists within the CDAO and CAIO Offices.

  • Cultivate collegial partnerships with Emory University research, IT, and academic groups to build consensus and drive shared AI/data initiatives.

  • Collaborate with external organizations to source and leverage third-party data assets that augment institutional analytics capabilities.

  • Present complex data findings, AI model outputs, and strategic recommendations to senior leadership, boards, and clinical decision-makers with clarity and conviction.

 
PREFERRED QUALIFICATIONS:
  • Master's degree in Data Science, Computer Science, Statistics, Engineering, or a related quantitative field; advanced degree (PhD) strongly preferred.
  • 10+ years of progressive experience in data science, advanced analytics, or AI/ML leadership — with at least 3 years managing technical teams in an enterprise environment.
  • Demonstrated expertise in machine learning lifecycle management (MLOps): model development, deployment, monitoring, and governance at scale.
  • Hands-on experience with cloud-native data platforms; Microsoft Fabric, Azure ML, or equivalent modern data lakehouse/warehouse architectures.
  • Proficiency in business intelligence and data visualization (Power BI strongly preferred); experience with APIs, Python/R, and large-scale SQL-based analytics.
  • Strong understanding of data governance, ethical use of Data/AI, and privacy regulations (HIPAA experience is a significant plus).
  • Exceptional executive communication skills — ability to synthesize technical complexity into strategic narrative for C-suite and Board-level audiences.
  • Proven ability to lead cross-functional, matrixed teams and collaborate effectively with both business users and technical engineering teams.
  • Experience with Generative AI / Large Language Model (LLM) deployment: prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and LLMOps.
  • Background in healthcare or life sciences data — including clinical data models (FHIR, HL7, OMOP) and EHR analytics.
  • Experience with Microsoft Fabric (Lakehouse, Warehouse, Data Factory, Real-Time Intelligence, Fabric AI/Copilot) in a production enterprise setting.
  • Familiarity with responsible AI frameworks, bias detection, explainability (XAI), and AI ethics policy development.
  • Experience building and scaling data science and ML platforms in regulated or academic health environments.
  • Certification in cloud platforms (Azure, AWS, GCP), data governance frameworks (DAMA-DMBOK), or AI/ML (Google ML, AWS ML Specialty, etc.).
 
MINIMUM QUALIFICATIONS:
  • Bachelor's degree in data science, engineering, statistics, analytics or related areas, and ten years of related experience, OR an equivalent combination of experience, education, and training.
  • Knowledge about health, research, and/or academic programs.
  • Excellent communication skills and experience presenting findings to decision-makers.
  • Ability to collaborate with senior leadership, work effectively and independently on multiple priorities with strict deliverable dates.
  • Experience working with both business users and technical development teams. Experience with APIs, business intelligence, and data visualization tools (experience WebGIS, RShiny, and/or Microsoft Power BI highly preferred).
  • Experience with Cloud environment and services.
  • Familiarity with data protection and privacy, data ethics, and data governance issues.
  • Strong, demonstrated skills in writing and presentation of findings and analyses.
  • Experience working with large structured and unstructured datasets and telling a compelling story that tracks to value.

Emory is an equal opportunity employer, and qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status or other characteristics protected by state or federal law.

Emory Healthcare is committed to providing reasonable accommodations to qualified individuals with disabilities upon request. Please contact Emory Healthcare’s Human Resources at careers@emoryhealthcare.org. Please note that one week's advance notice is preferred.

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