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Mlops Data Engineer Jobs in Indiana (NOW HIRING)

Deloitte

AI Data Engineer - Manager

Indianapolis, IN · On-site

$109.40K - $131.40K/yr

AI Data Engineer - Manager Our Human Capital practice is at the forefront of transforming the ... Understand and contribute to MLOps and LLMOps, focusing on operational capabilities and ...

Deloitte

AI Data Engineer Manager

Indianapolis, IN · On-site

$109.40K - $131.40K/yr

AI Data Engineer Manager Position Summary Our Human Capital practice is at the forefront of ... Understand and contribute to MLOps and LLMOps, focusing on operational capabilities and ...

Deloitte

AI Data Engineer - Senior Consultant

Indianapolis, IN · Hybrid

$99.90K - $137.20K/yr

You will work with an AI Data Engineer (data ingestion, curation, governance, platform foundations ... Contribute to MLOps/LLMOps and production operations (versioning, reproducibility, CI/CD, automated ...

Deloitte

AI Engineer Senior Consultant

Indianapolis, IN · Hybrid

$99.90K - $137.20K/yr

... and HR data domains. * 4+ years of experience operationalizing LLMOps/MLOps capabilities ... AI Engineer Senior Consultant Our Deloitte Human Capital team transforms technology platforms ...

Guidehouse

Data Architect

Indianapolis, IN

$61 - $78.50/hr

Job Family: Data Engineering & Architecture Consulting Travel Required: Up to 25% Clearance ... Partner with AI/ML, MLOps, and analytics teams to enable productiongrade model development and ...

Deloitte

AI Engineer Senior Consultant

Indianapolis, IN · Hybrid

$99.90K - $137.20K/yr

... and HR data domains. * 4+ years of experience operationalizing LLMOps/MLOps capabilities ... AI Data Engineer Senior Consultant Position Summary Our Deloitte Human Capital team transforms ...

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Mlops Data Engineer information

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

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 are MLOps Data Engineers?

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 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.

What are popular job titles related to Mlops Data Engineer jobs in Indiana? For Mlops Data Engineer jobs in Indiana, the most frequently searched job titles are:
What job categories do people searching Mlops Data Engineer jobs in Indiana look for? The top searched job categories for Mlops Data Engineer jobs in Indiana are:
What cities in Indiana are hiring for Mlops Data Engineer jobs? Cities in Indiana with the most Mlops Data Engineer job openings:
Mlops Data Engineer jobs near you

Information Technology - BI Data Architect

TCC, Verizon Authorized Retailer

Fishers, IN

$59.75 - $76.75/hr

Full-time

Posted 22 hours ago

Job description


PURPOSE

As a key member of the Data Analytics and Reporting team, the Data Architect will lead the design, implementation, and optimization of an enterprise data platform built on the Microsoft Fabric ecosystem. This role is responsible for translating business and analytical requirements into scalable, secure, and high-performance data architectures that enable advanced analytics, AI-driven insights, and enterprise reporting.

The ideal candidate will possess deep expertise in Microsoft Fabric, modern cloud data engineering, and advanced analytics technologies. They will drive the evolution of Lakehouse, warehouse, and semantic models while enabling AI and machine learning workloads through trusted, governed, and high-quality data assets.

This role partners closely with data engineers, analytics developers, data scientists, and business stakeholders to ensure the organization’s data platform supports predictive analytics, automation, and intelligent decision-making.

Essential Duties and Responsibilities

  • Lead the architecture, design, and governance of the Microsoft Fabric environment, including Lakehouse, Data Warehouse, OneLake, and Semantic Models.
  • Design and maintain scalable data models optimized for BI, advanced analytics, and AI workloads.
  • Architect end-to-end data pipelines using Fabric Data Factory, notebooks, and streaming capabilities.
  • Implement and optimize data ingestion, transformation, and orchestration processes for structured and unstructured data.
  • Enable advanced analytics, machine learning, and AI initiatives through well-designed data foundations and feature stores.
  • Ensure quality, consistency and security of database design, and ability to meet enterprise requirements in alignment with DBA, Enterprise Architect, and Integration teams
  • Establish best practices for data modeling, performance tuning, security, and cost optimization within Fabric.
  • Develop and maintain enterprise metadata, lineage, and data cataloging using Microsoft Purview and Fabric governance tools.
  • Collaborate with data science teams to support model training, deployment, and monitoring.
  • Review and govern ETL/ELT processes, notebooks, and transformation logic.
  • Ensure compliance with data security, privacy, and regulatory requirements.
  • Lead architectural reviews and approve platform changes to ensure stability and integrity.
  • Translate complex business and analytical needs into technical architecture and implementation plans.
  • Mentor and guide data engineers, BI developers, and analytics professionals.
  • Support real-time and near-real-time analytics use cases.
  • Drive adoption of AI-powered analytics, Copilot, and intelligent reporting capabilities.
  • Continuously evaluate emerging data, analytics, and AI technologies and recommend improvements.

Required Knowledge, Skills, and Abilities

Technical Expertise

  • Strong hands-on experience with Microsoft Fabric (Lakehouse, Warehouse, Data Factory, Notebooks, Semantic Models).
  • Advanced proficiency in Azure data services and cloud-native architecture.
  • Expertise in modern data modeling (dimensional, data vault, Lakehouse, feature engineering).
  • Experience supporting machine learning, AI, and advanced analytics pipelines.
  • Proficiency in SQL, PySpark, and Python for analytics and data engineering.
  • Knowledge of MLOps, data versioning, and model lifecycle management.
  • Experience with data governance, lineage, and cataloging platforms.
  • Strong understanding of data security, identity management, and compliance in cloud environments.
  • Experience integrating external AI/ML platforms and APIs.

Professional Skills

  • Strategic thinker with ability to align data architecture with business and AI initiatives.
  • Strong communication skills for technical and executive audiences.
  • Proven ability to lead cross-functional data and analytics projects.
  • Analytical and problem-solving mindset.
  • Ability to manage multiple priorities in a fast-paced, innovation-driven environment.
  • Collaborative leadership style and mentoring capability.
  • Innovative mindset with strong interest in emerging AI technologies.

Education and Experience

  • Bachelor’s degree in Computer Science, Data Science, Information Systems, or related field (or equivalent experience).
  • 10+ years of experience in data architecture, engineering, or analytics platforms.
  • 5+ years of experience designing cloud-based data platforms.
  • 3+ years of hands-on experience with Microsoft Fabric and/or modern Azure analytics services.
  • Demonstrated experience supporting advanced analytics, AI, or machine learning solutions.
  • Experience working in Agile/SCRUM environments.

Preferred Technologies

  • Microsoft Fabric
  • Azure Data Factory
  • Azure Synapse Analytics
  • Azure Databricks
  • Power BI
  • Microsoft Purview
  • Azure Machine Learning
  • Python / Py Spark
  • Azure DevOps
  • Git

"The Cellular Connection is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, or protected veteran status"

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