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Kubeflow Mlflow Jobs in Spring, TX (NOW HIRING)

Hexagon AB

Lead Data Scientist

Houston, TX · On-site

End-to-end experience with ML pipelines, experiment tracking (MLflow, W&B), model versioning ... Familiarity with big data tools (Spark, Databricks, Snowflake), orchestration (Airflow, Kubeflow ...

Hexagon AB

Principal AI/ML Software Engineer

Houston, TX · On-site

$128K - $172K/yr

... MLflow, and pytest • SQL: Advanced proficiency with complex queries, window functions, and ... Kubeflow), and monitoring tools (Datadog, Prometheus) • Experience with LLM fine-tuning, document ...

All JobsKubeflow Mlflow JobsKubeflow Mlflow Jobs in Spring, TX

Kubeflow Mlflow information

What are Kubeflow and MLflow?

Kubeflow and MLflow are open-source platforms designed to simplify and automate machine learning workflows. Kubeflow focuses on running scalable and portable machine learning (ML) workloads on Kubernetes, providing tools for model training, deployment, and management. MLflow, on the other hand, is a platform for managing the ML lifecycle, including experiment tracking, model versioning, and deployment. Both tools can be integrated to streamline developing, tracking, and deploying ML models in production environments.

How do professionals working with Kubeflow and MLflow typically collaborate with data scientists and DevOps teams?

Professionals utilizing Kubeflow and MLflow often serve as a bridge between data scientists, who focus on model development, and DevOps teams, who manage infrastructure and deployment. They facilitate seamless model training, versioning, and deployment pipelines by integrating these tools into the workflow. Collaboration involves regular communication to ensure that models are production-ready, reproducible, and scalable, as well as troubleshooting any pipeline or integration issues that arise. This role requires adaptability and strong teamwork skills to align technical requirements and project goals across departments.

What are the key skills and qualifications needed to thrive as a Kubeflow/MLflow engineer, and why are they important?

To excel as a Kubeflow/Mlflow Engineer, you need a strong background in machine learning lifecycle management, DevOps practices, and cloud-native technologies, typically supported by a degree in computer science or related fields. Hands-on experience with Kubernetes, Docker, Kubeflow, Mlflow, and familiarity with cloud platforms like AWS, GCP, or Azure is highly valuable, along with relevant certifications. Excellent problem-solving, collaboration, and communication skills help you integrate complex workflows and work effectively with data scientists and engineering teams. These capabilities ensure scalable, reliable, and efficient deployment and monitoring of machine learning models in production environments.

What is the difference between Kubeflow Mlflow vs Data Scientist?

AspectKubeflow MlflowData Scientist
Primary FocusMachine learning workflows, deployment, and managementData analysis, modeling, and insights
Required SkillsML Ops, cloud platforms, containerization, PythonStatistics, programming, data visualization
Work EnvironmentCloud-based, DevOps-orientedResearch, analytics, business insights
CertificationsML certifications, cloud certificationsData science, analytics certifications

While Kubeflow Mlflow focuses on managing and deploying machine learning models in production environments, Data Scientists primarily analyze data, build models, and generate insights. Both roles often collaborate but serve different stages of the ML lifecycle.

What are popular job titles related to Kubeflow Mlflow jobs in Spring, TX?

For Kubeflow Mlflow jobs in Spring, TX, the most frequently searched job titles are:

What job categories do people searching Kubeflow Mlflow jobs in Spring, TX look for?

The top searched job categories for Kubeflow Mlflow jobs in Spring, TX are:

What cities near Spring, TX are hiring for Kubeflow Mlflow jobs?

Cities near Spring, TX with the most Kubeflow Mlflow job openings:

Kubeflow Mlflow jobs near you
Infographic showing various Kubeflow Mlflow job openings in Spring, TX as of August 2026, with employment types broken down into 89% Full Time, 5% Part Time, and 6% Contract. Highlights an 81% Physical, 4% Hybrid, and 15% Remote job distribution.

Lead Data Scientist

Hexagon AB

Houston, TX • On-site

Full-time

Re-posted 16 hours ago

Job description

Lead Data Scientist
Job Location (Short): Houston, Texas-USA | Madison, Alabama-USA | Roanoke, Virginia-USA
Workplace Type: Remote
Req Id: 2289
Responsibilities
Octave's ETQ division is seeking a hands-on Data Scientist to build predictive models, implement Generative AI and Agentic AI features, and architect data-driven solutions for our document-based compliance management platform. This role requires a technical expert who can develop, deploy, and maintain ML systems in production environments.
  • Build and deploy Generative AI features using foundation models (AWS Bedrock, OpenAI, Anthropic Claude) and RAG architectures with vector databases for compliance document understanding
  • Design agentic AI systems that autonomously handle compliance workflows, document review, regulatory mapping, and multi-step reasoning tasks
  • Implement comprehensive LLM evaluation frameworks with automated pipelines, custom metrics, benchmark datasets, and safety guardrails ensuring regulatory compliance
  • Build end-to-end MLOps pipelines for model training, deployment, monitoring, versioning, and automated retraining with drift detection
  • Develop predictive models for compliance risk scoring, regulatory change impact, anomaly detection, and time-series forecasting
  • Write production-quality Python code for data processing, feature engineering, API development (FastAPI/Flask), and ETL/ELT workflows
  • Lead A/B experiments and product analytics to measure AI feature impact and drive data-driven decision-making
  • Create explainability frameworks (SHAP/LIME) and monitoring dashboards ensuring transparency and regulatory adherence
  • Collaborate with cross-functional teams to translate business needs into ML solutions and communicate insights to stakeholders

Python (5+ years): Production-level experience with Pandas, NumPy, scikit-learn, XGBoost, TensorFlow/PyTorch, Hugging Face Transformers, FastAPI/Flask, MLflow, and pytest
SQL: Advanced proficiency with complex queries, window functions, and optimization
Machine Learning & NLP: Strong foundation in supervised/unsupervised learning, deep learning, document understanding, text classification, and semantic analysis
Generative AI & LLMs: Hands-on experience with foundation models (GPT, Claude, Llama), prompt engineering, RAG architectures, and vector databases (Pinecone, Weaviate, Chroma)
MLOps & ModelOps: End-to-end experience with ML pipelines, experiment tracking (MLflow, W&B), model versioning, feature stores, drift detection, CI/CD for ML, and Docker containerization
LLM Evaluation: Experience with evaluation frameworks (RAGAS, DeepEval), custom metrics, benchmark datasets, and human-in-the-loop validation
Cloud & AWS: Experience with AWS services including SageMaker, Bedrock, S3, Lambda, EC2, and CloudWatch
Statistics & Experimentation: Strong foundation in statistics, A/B testing, causal inference, and experimental design
Visualization: Proficiency with Tableau, Power BI, or Python visualization libraries
Education / Qualifications
Experience & Education
  • 7+ years in data science, ML engineering, or related roles
  • 3+ years building NLP/generative AI applications and implementing MLOps in production
  • Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or related field (PhD preferred)
  • Track record of deploying ML systems processing large-scale datasets with proper monitoring and governance

Preferred Qualifications
  • Experience with agentic AI frameworks (LangGraph, LangChain, AutoGen, CrewAI)
  • Knowledge of Life Sciences/regulated industries (FDA, EMA, ISO, GxP) and compliance management systems
  • Familiarity with big data tools (Spark, Databricks, Snowflake), orchestration (Airflow, Kubeflow), and monitoring tools (Datadog, Prometheus)
  • Experience with LLM fine-tuning, document processing libraries, multi-modal AI, or distributed training
  • Understanding of ML governance, bias detection, model risk management, and data privacy regulations (GDPR, CCPA, HIPAA)
  • Experience working in agile environments with Jira
  • AWS ML certifications or similar credentials

Key Competencies
  • Strong communication skills explaining complex models to technical and non-technical audiences
  • Ability to work independently and collaboratively in fast-paced environments
  • Proven ability to convert POCs into production-grade solutions
  • Understanding of ethical AI and building trustworthy, explainable systems for regulated environments

#LI-PB1 LI-Remote
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About Octave
Octave provides mission-critical software that empowers organizations to make informed decisions across every stage of the asset lifecycle - Design, Build, Operate and Protect - where performance, safety, and reliability are non-negotiable and failure is not an option.
Turning complex operational data into actionable intelligence, Octave connects expertise, real-world conditions and enterprise-scale insight to improve performance, resilience and incident response where it matters most.
Octave has approximately 7,200 employees in 45 countries. Learn more at octave.com and follow us on LinkedIn.
Why work for Octave?
All in. Always forward. That's the way we do things around here. We put trust in our people because we believe it's the best way to unleash potential, bring ideas to life, and keep moving ahead. And it's why we're committed to creating an environment that's truly supportive, providing you with the resources you need to support your ambitions, no matter who you are or where you are in the world.
Everyone is welcome
At Octave, we believe that diverse and inclusive teams are critical to the success of our people and our business. Here, everyone is welcome. As an inclusive workplace, we don't discriminate. In fact, we embrace differences and are fully committed to creating equal opportunities, an inclusive environment, and fairness for all.
Respect is the cornerstone of how we operate, so speak up and be yourself. You're valued here.
Recruitment Fraud Alert
Octave posts all official job opportunities on either https://careers.octave.com/ or https://www.octave.com/about/careers and communicates only from email addresses ending in @octave.com. We never request payment or personal banking information during recruitment. No offers will ever be extended without a proper interview via Teams or in person, never done over email alone. If you suspect fraud, it probably is, and contact us at careers@octave.com

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