Cerebra Consulting Inc
Cerebra Consulting Inc

Senior ML Engineer - Onsite

Austin, TX, US • Posted 1 day ago • Updated 2 hours ago
Contract Independent
Contract Corp To Corp
Contract W2
On-site
Fitment

Dice Job Match Score™

👾 Reticulating splines...

Job Details

Skills

  • automation
  • Unit testing
  • GIT
  • Manufacturing
  • Machine learning
  • Software Engineering
  • Workflows
  • Batch processing
  • Data cleansing
  • Databricks
  • Digital Products
  • Continuous Integration
  • REQUIREMENTS ANALYSIS
  • Data Validation
  • DATA SCIENCE
  • Risk Analysis
  • Cloud Engineering
  • Data Pipelines
  • Git Flow
  • Infrastructure Management
  • Reliability
  • Information Technology
  • Python (Programming Language)
  • Testing Skills
  • Monitoring of Systems
  • Software Quality
  • Lifecycle Management
  • Management of Stress
  • Governance Risk Management and Compliance
  • Automation of Tests
  • Information Engineering
  • Execution of Experiments
  • Machine Learning Operations
  • Reproducibility
  • Delivery Pipeline
  • Management of Software Versions
  • Sales Promotion
  • Internet of Things
  • Feature Engineering
  • Model Building
  • Operational Support and Analysis
  • Quality Auditing
  • Proof of Concept

Summary

Cerebra Consulting Inc is a System Integrator and IT Services Solution provider with a focus on Big Data, Business Analytics, Cloud Solutions, Amazon Web Services, Salesforce, Oracle EBS, Peoplesoft, Hyperion, Oracle Configurator, Oracle CPQ, Oracle PLM and Custom Application Development. Utilizing solid business experience, industry-specific expertise, and proven methodologies, we consistently deliver measurable results for our customers. Cerebra has partnered with leading enterprise software companies and cloud providers such as Oracle, Salesforce, Amazon and able to leverage these partner relationships to deliver high-quality, end-to-end customer solutions that are targeted to the needs of each customer.

Hello,

Hope you are doing well,

Role: Senior ML Engineer Deployment and Databricks MLOps

Duration: 3 month contract, can be extended

Location: Austin, TX 4-5 days per week

Role summary

We are looking for a hands-on Senior ML Engineer to help productionize machine learning solutions for manufacturing use cases involving deployment and pipeline buildout. This role will sit at the intersection of model operationalization, data/feature pipelines, and CI/CD, helping us move from proof of concept to repeatable, governed, production-ready delivery. The role is aligned to our current direction of hardening Databricks-based MLOps infrastructure, MLflow-based lifecycle management, and CI/CD-driven promotion of models and workflows into operational use.

What this role will do

  • Build and operationalize ML pipelines in Databricks to support training, validation, batch scoring, and deployment workflows.
  • Implement and maintain CI/CD pipelines for ML code, data pipelines, and model promotion using Git-driven development practices and automated quality checks.
  • Partner with data scientists and data engineers to turn experimental models into production candidates with clear dependencies, reproducible artifacts, and governed deployment paths.
  • Build and manage feature/data pipelines that support model retraining, re-scoring, monitoring, and downstream consumption.
  • Establish model lifecycle controls using MLflow and Unity Catalog, including experiment tracking, model registration, versioning, lineage, and controlled promotion across environments.
  • Improve reliability of ML systems through data validation, testing, monitoring, and automation that reduce manual intervention and deployment risk.
  • Support deployment patterns that can extend from lab and cloud development into plant-ready operational workflows over time.

Key responsibilities

  • Productionize machine learning models developed by the data science team for manufacturing applications.
  • Design, build, and maintain reusable ML workflows for data preparation, feature engineering, model training, evaluation, deployment, and inference.
  • Own CI/CD patterns for ML and pipeline assets, including unit tests, smoke tests, code quality checks, and release automation.
  • Manage Databricks jobs and workflows for retraining, scoring, orchestration, and scheduled execution.
  • Package and promote versioned model artifacts with traceability to code commits, data snapshots, and registry versions.
  • Collaborate across ML, data engineering, cloud/platform, and manufacturing stakeholders to ensure deployed solutions are scalable, supportable, and aligned to production constraints.

Required qualifications

  • Bachelor's, Master's, or equivalent experience in Computer Science, Data Science, Engineering, or a related technical field.
  • Strong software engineering skills in Python and production-quality development practices.
  • Experience deploying machine learning models into production environments.
  • Strong experience with Databricks, including jobs/workflows, repos, and MLflow-based experimentation and model lifecycle management.
  • Experience building CI/CD pipelines for ML or data products using Git-based workflows and automated testing.
  • Strong understanding of data pipelines, feature engineering, batch processing, and pipeline orchestration.
  • Experience working across model development, deployment, and operational support in cross-functional environments.

Preferred qualifications

  • Experience with manufacturing, industrial IoT, quality, or plant-floor analytics use cases.
  • Experience with model governance, lineage, reproducibility, and controlled promotion of ML assets across environments.
  • Experience designing resilient ML pipelines that can handle changing upstream data conditions and retraining needs.
  • Familiarity with model monitoring, validation checks, and operational observability.
  • Experience supporting the transition from PoC or R&D models into production-ready solution patterns.

What success looks like

In the near term, this person will help establish a repeatable path to deploy and operate manufacturing ML solutions on Databricks, including model lifecycle management, underlying pipelines, and CI/CD automation. Over time, the role should help create a reusable template that bridges experimentation, deployment, and governed operations across multiple manufacturing AI/ML use cases.

Short posting version

We are seeking a Senior ML Engineer to help deploy machine learning models and build the Databricks-based MLOps, pipeline, and CI/CD foundation behind them. This person will partner with data scientists, data engineers, and platform teams to productionize manufacturing AI/ML solutions through MLflow-based model lifecycle management, automated workflows, governed releases, and scalable data/feature pipelines.

Thanks,

Sudhanshu Srivastava

Email -

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
  • Dice Id: 10215820
  • Position Id: 2026-61812
  • Posted 1 day ago
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