ZipRecruiter

Log In

1

Mosaic Ml Jobs (NOW HIRING)

Radiology Partners

Product Manager

$150K - $200K/yr

Mosaic Clinical Technologies™ is building technology that evolves with clinicians to advance ... Collaborate closely with engineering, AI/ML, design, and clinical teams to translate complex health ...

radiology partners

Product Manager

$150K - $200K/yr

Remote WHO WE ARE AND WHAT WE DO Mosaic Clinical Technologies™ is pioneering a new imaging ... Collaborate closely with engineering, AI/ML, design, and clinical teams to translate complex health ...

Radiology Partners

VP, EMR Data Strategy and Alliances

$250K - $280K/yr

Mosaic Clinical Technologies™ is building technology that evolves with clinicians to advance ... Experience with AI/ML-enabled healthcare solutions, PACS/Imaging, clinical decision support, or ...

Radiology Partners

Product Manager

$150K - $200K/yr

Mosaic Clinical Technologies is building technology that evolves with clinicians to advance ... Collaborate closely with engineering, AI/ML, design, and clinical teams to translate complex health ...

radiology partners

VP, EMR Data Strategy and Alliances

$250K - $280K/yr

Remote WHO WE ARE AND WHAT WE DO Mosaic Clinical Technologies™ is pioneering a new imaging ... Experience with AI/ML-enabled healthcare solutions, PACS/Imaging, clinical decision support, or ...

Jobtailor

Data Scientist

Raleigh, NC · On-site

$110 - $170/hr

Exposure to Databricks ML ecosystem (Feature Store, Experiment Track, Model Serving, Mosaic AI) and MLflow. * Familiarity with distributed computing concepts - PySpark, Optuna/Ray, Spark SQL ...

New

subway

Sr Data Engineer

Shelton, CT

$106K - $144K/yr

Build GenAI and ML enablement patterns (RAG, feature stores, semantic layers) using Databricks Mosaic AI or Snowflake Cortex. * Partner with Data Science and Analytics teams to operationalize models ...

subway

Sr Data Engineer

Shelton, CT

$106K - $144K/yr

Build GenAI and ML enablement patterns (RAG, feature stores, semantic layers) using Databricks Mosaic AI or Snowflake Cortex. * Partner with Data Science and Analytics teams to operationalize models ...

subway

Sr Data Engineer

Shelton, CT · On-site

$106K - $144K/yr

Build GenAI and ML enablement patterns (RAG, feature stores, semantic layers) using Databricks Mosaic AI or Snowflake Cortex. * Partner with Data Science and Analytics teams to operationalize models ...

The Subway

Sr Data Engineer

Shelton, CT · On-site

$114K - $137K/yr

Build GenAI and ML enablement patterns (RAG, feature stores, semantic layers) using Databricks Mosaic AI or Snowflake Cortex. * Partner with Data Science and Analytics teams to operationalize models ...

subway

Sr Data Engineer

Shelton, CT · On-site

$106K - $144K/yr

Build GenAI and ML enablement patterns (RAG, feature stores, semantic layers) using Databricks Mosaic AI or Snowflake Cortex. * Partner with Data Science and Analytics teams to operationalize models ...

previous pagenext page

Showing results 21-40

All JobsMosaic Ml Jobs

Mosaic Ml information

See salary details

$59.5K

$111.6K

$203K

How much do mosaic ml jobs pay per year?

As of Aug 8, 2026, the average yearly pay for mosaic ml in the United States is $111,632.00, according to ZipRecruiter salary data. Most workers in this role earn between $80,500.00 and $132,500.00 per year, depending on experience, location, and employer.

What is the difference between Mosaic Ml vs Data Analyst?

AspectMosaic MlData Analyst
Required CredentialsTypically requires machine learning, data science, or related certificationsOften requires statistics, data analysis, or business intelligence certifications
Work EnvironmentFocuses on developing ML models, coding, and algorithm optimizationInvolves data interpretation, reporting, and business insights
Employer & Industry UsageUsed in tech, finance, and data-driven companies for predictive modelingCommon across various industries for data reporting and decision support

While both roles work with data, Mosaic Ml primarily focuses on building and deploying machine learning models, requiring coding and algorithm skills. Data Analysts interpret data and generate reports to support business decisions. Understanding these differences helps in choosing the right career path or job search focus.

What is Mosaic ML?

Mosaic ML is a company that specializes in developing tools and infrastructure to make training large-scale machine learning models more efficient, affordable, and accessible. They provide a platform and software suite that allows organizations to customize, train, and deploy state-of-the-art AI models, including large language models (LLMs), without the massive computational resources typically required. Mosaic ML focuses on optimization techniques that reduce training time and cost, making advanced AI more attainable for businesses and research teams. Their technology is used across industries for tasks like natural language processing, computer vision, and generative AI applications.

What does Mosaic ML do?

Mosaic ML is a company that provides tools and infrastructure to help organizations train large-scale machine learning models more efficiently and cost-effectively. They focus on optimizing distributed training processes, often utilizing cloud resources and advanced algorithms to improve performance. Job roles at Mosaic ML may require knowledge of machine learning frameworks, distributed systems, and programming skills in Python or similar languages.

How does a Mosaic ML engineer typically collaborate with data scientists and product teams during a project?

Mosaic ML engineers work closely with data scientists to design, scale, and optimize machine learning models, ensuring they meet both technical and business requirements. They frequently participate in cross-functional meetings to align model development with product objectives, while also translating complex technical concepts for non-technical stakeholders. Collaboration often involves iterative feedback, code reviews, and the joint troubleshooting of model deployment issues, fostering a dynamic and supportive team environment. This teamwork not only accelerates project delivery but also offers engineers valuable exposure to different perspectives and skill sets.

What are the key skills and qualifications needed to thrive as a machine learning engineer at Mosaic ML, and why are they important?

To thrive as a Machine Learning Engineer at MosaicML, you need a strong background in computer science, statistics, and deep learning, often supported by a relevant degree and experience with large-scale model training. Familiarity with ML frameworks such as PyTorch or TensorFlow, distributed computing systems, and version control tools like Git is typically required. Strong problem-solving skills, teamwork, and effective communication help you collaborate and adapt in a fast-evolving field. These skills are crucial for building efficient machine learning systems and contributing to innovative AI research and deployment.
More about Mosaic Ml jobs
What cities are hiring for Mosaic Ml jobs? Cities with the most Mosaic Ml job openings:
What states have the most Mosaic Ml jobs? States with the most job openings for Mosaic Ml jobs include:
What job categories do people searching Mosaic Ml jobs look for? The top searched job categories for Mosaic Ml jobs are:
Mosaic Ml jobs near you
Infographic showing various Mosaic Ml job openings in the United States as of August 2026, with employment types broken down into 94% Full Time, 2% Part Time, and 4% Contract. Highlights an 82% Physical, 4% Hybrid, and 14% Remote job distribution, with an average salary of $111,632 per year, or $53.7 per hour.

Principal Observability Architect (Splunk & Databricks)

Scicom Infrastructure Services

Atlanta, GA • Remote

Other

Re-posted 4 days ago

Job description

Salary:

Position Summary


We are seeking a highly experienced Principal Observability Architect to lead the design, implementation, modernization, and optimization of enterprise-scale observability and analytics platforms. This role will serve as the technical authority for log management, observability engineering, telemetry pipelines, AIOps, security analytics, and data lakehouse architectures leveraging Splunk, Databricks, Cribl, OpenTelemetry, and cloud-native technologies.

The ideal candidate possesses deep expertise in traditional observability platforms (Splunk, Dynatrace, AppDynamics, ServiceNow ITOM) and modern data lakehouse architectures utilizing Databricks, Delta Lake, Unity Catalog, and AI/ML-driven analytics. This individual will drive the strategic transformation from legacy SIEM and observability platforms toward scalable, cloud-native observability data lakes.


Key Responsibilities


Enterprise Architecture & Strategy

  • Define enterprise observability architecture standards, patterns, and roadmaps.
  • Lead observability transformation initiatives involving Splunk modernization and Databricks adoption.
  • Develop reference architectures for telemetry ingestion, storage, analytics, security, and AI-driven operations.
  • Align observability strategies with business, security, compliance, and operational objectives.
  • Create executive-level architecture presentations, business cases, and technology roadmaps.


Splunk Platform Leadership

  • Architect large-scale Splunk Enterprise and Splunk Cloud environments.
  • Design and optimize:
    • Indexer clusters
    • Search head clusters
    • Forwarder architectures
    • Deployment servers
    • Data models
    • ITSI implementations
  • Define ingestion, retention, indexing, and data lifecycle strategies.
  • Lead migration initiatives involving:
    • Splunk to Databricks
    • Heavy Forwarders to Cribl
    • SIEM modernization programs
  • Optimize SPL searches, data models, summary indexing, and dashboard performance.


Databricks & Lakehouse Architecture

  • Architect enterprise observability data lake solutions using:
    • Databricks Lakehouse
    • Delta Lake
    • Unity Catalog
    • Delta Live Tables
    • Structured Streaming
    • Mosaic AI
    • Genie
  • Design Medallion Architectures:
    • Bronze
    • Silver
    • Gold
  • Develop governance strategies including:
    • RBAC
    • Data masking
    • Data lineage
    • Audit controls
  • Create high-performance log analytics solutions capable of supporting petabyte-scale telemetry environments.
  • Enable self-service analytics and AI-powered observability use cases.


Telemetry & Data Engineering

  • Design ingestion architectures supporting:
    • OpenTelemetry
    • OCSF
    • Syslog
    • Kafka
    • Azure Event Hubs
    • AWS Kinesis
    • GCP Pub/Sub
    • Cribl
  • Define normalization and enrichment frameworks.
  • Establish data quality and schema management processes.
  • Design real-time and batch processing pipelines.


AIOps & Advanced Analytics

  • Lead implementation of:
    • AIOps
    • Predictive analytics
    • Root cause analysis
    • Anomaly detection
    • Event correlation
  • Integrate observability datasets with AI/ML platforms.
  • Develop observability use cases leveraging:
    • Mosaic AI
    • Agentic AI
    • LLMs
    • Generative AI
  • Build operational intelligence and executive KPI dashboards.


Security & Compliance

  • Architect observability solutions supporting:
    • SOC operations
    • Threat hunting
    • Security analytics
    • Compliance reporting
  • Design frameworks aligned with:
    • HIPAA
    • PCI-DSS
    • SOX
    • NIST
    • ISO 27001
  • Implement data governance and security controls across observability platforms.


Leadership & Governance

  • Provide technical leadership to engineering teams.
  • Mentor architects, engineers, and developers.
  • Conduct architecture reviews and design governance.
  • Define platform standards, best practices, and operational procedures.
  • Engage directly with executive stakeholders and business leaders.


Required Qualifications


Experience

  • 10+ years of experience in Enterprise Observability, Monitoring, or Security Analytics.
  • 5+ years architecting large-scale Splunk environments.
  • 3+ years designing Databricks Lakehouse architectures.
  • Experience managing environments exceeding:
    • 50 TB/day preferred
    • 100+ TB/day strongly preferred
  • Experience leading enterprise transformation programs.


Splunk Expertise

Deep expertise in:

  • Splunk Enterprise
  • Splunk Cloud
  • Splunk ITSI
  • Enterprise Security
  • SPL Development
  • Data Models
  • Indexer Clustering
  • Search Head Clustering
  • SmartStore
  • Heavy Forwarders
  • Universal Forwarders

Databricks Expertise

Strong experience with:

  • Databricks Lakehouse
  • Delta Lake
  • Unity Catalog
  • Delta Live Tables
  • Structured Streaming
  • Databricks SQL
  • Genie
  • Mosaic AI
  • Lakehouse Federation

Cloud Platforms

Experience with one or more:

  • Microsoft Azure
  • Amazon Web Services
  • Google Cloud

Data Technologies

Strong knowledge of:

  • Kafka
  • OpenTelemetry
  • OCSF
  • Iceberg
  • Spark
  • SQL
  • Python
  • REST APIs
  • Event Streaming Architectures


Preferred Qualifications

  • Experience with Cribl Stream and Cribl Edge
  • Experience with Dynatrace, AppDynamics, Datadog, or New Relic
  • Experience with ServiceNow ITOM/Event Management
  • Experience designing AI/ML operational analytics solutions
  • Experience with Security Data Lakes and SIEM modernization initiatives
  • Experience with FinOps and cloud cost optimization
  • Experience building observability platforms for healthcare, financial services, retail, or large enterprise organizations


Certifications (Preferred)

Splunk

  • Splunk Enterprise Certified Architect
  • Splunk Core Certified Consultant

Databricks

  • Databricks Certified Data Engineer Professional
  • Databricks Certified Solutions Architect

Cloud

  • Azure Solutions Architect Expert
  • AWS Solutions Architect Professional
  • Google Professional Cloud Architect


Success Metrics

Within the first 12 months, the architect will:

  • Deliver enterprise observability architecture roadmap.
  • Reduce observability platform costs through modernization initiatives.
  • Design and implement a scalable observability data lake architecture.
  • Improve telemetry ingestion performance and reliability.
  • Enable AI-powered analytics and operational intelligence capabilities.
  • Establish enterprise governance standards for observability and security telemetry.
  • Support petabyte-scale observability and security analytics workloads.


Ideal Background

Candidates from organizations utilizing large-scale observability environments such as healthcare, banking, retail, telecommunications, logistics, cloud providers, or managed services organizations are highly desirable. Experience supporting environments generating 100TB+ of telemetry per day and integrating Splunk, Databricks, Cribl, OpenTelemetry, and cloud-native data platforms is strongly preferred.