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Ml Engineer Jobs in Spring, TX (NOW HIRING)

Principal AI/ML Software Engineer

Houston, TX ยท On-site

$124K - $167K/yr

Principal AI/ML Software Engineer Job Location (Short): Houston, Texas-USA | Madison, Alabama-USA Workplace Type: Remote Req Id: 2909 Responsibilities Position Overview We are seeking a motivated AI ...

Senior Advisor II, AI/ML Engineer

Houston, TX ยท On-site

$155K - $190K/yr

Phillips 66 & YOU - Together we can fuel the future As a Senior Advisor II, AIML Engineer, you will lead complex digital initiatives and AI/ML-driven innovation to advance enterprise efficiency and ...

Senior Advisor II, AI/ML Engineer

Houston, TX ยท On-site

$155K - $190K/yr

Phillips 66 & YOU - Together we can fuel the future As a Senior Advisor II, AIML Engineer, you will lead complex digital initiatives and AI/ML-driven innovation to advance enterprise efficiency and ...

AI/ML Engineer - Houston, Texas Only

Houston, TX ยท On-site

$104K - $125K/yr

Implements MLOps, LLMOps, AgentOps, DevOps, and DataOps practices including model evaluation ... Strong experience with AI/ML model lifecycle, required * Experience with enterprise AI platforms ...

Senior AI/ML Engineer

Houston, TX ยท On-site

$99K - $137K/yr

During phase 1 Halliburton will bring in their AI/ML team to help determine what they can extract ... for the business and engineering context, but with the AI team to understand their AI tools ...

AI ML Operations Engineer

Houston, TX ยท On-site

$66K - $89K/yr

Designs production-grade ML systems end-to-end (data โ†’ training โ†’ deployment โ†’ monitoring ... Strong data engineering fundamentals (pipeline reliability, data quality, versioning, and data)

AI/ML Platform Engineer

Spring, TX ยท On-site

$147K - $230K/yr

AI/ML Platform Engineer Description - We are a dynamic centralized platform team dedicated to harnessing cutting-edge AI/ML technology, particularly in the realm of Generative AI and large language ...

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Ml Engineer information

See Spring, TX salary details

$29.4K

$79.4K

$126.4K

How much do ml engineer jobs pay per year?

As of Jul 15, 2026, the average yearly pay for ml engineer in Spring, TX is $79,363.00, according to ZipRecruiter salary data. Most workers in this role earn between $59,200.00 and $97,000.00 per year, depending on experience, location, and employer.

What engineers make $500,000?

Senior engineers in fields like software engineering, data engineering, and machine learning engineering can earn $500,000 or more annually, especially with extensive experience, advanced skills, and in high-paying industries or companies. Compensation often includes base salary, bonuses, and stock options, particularly in tech giants or startups with significant growth potential.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as senior machine learning engineer or AI research director, often involving advanced skills in deep learning, data science, and programming with tools like Python and TensorFlow. Such roles usually require extensive experience, specialized knowledge, and may include leadership responsibilities or working in cutting-edge technology environments.

What does an ML engineer do?

An ML engineer designs, develops, and deploys machine learning models and algorithms to solve specific problems. They work with data preprocessing, model training, evaluation, and optimization, often using tools like Python, TensorFlow, or PyTorch. Their role involves integrating models into production systems and ensuring their performance and scalability.

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

To thrive as an ML Engineer, you need a solid background in mathematics, statistics, computer science, and experience with machine learning algorithms, often supported by a degree in a related field. Familiarity with programming languages like Python or R, ML frameworks such as TensorFlow or PyTorch, and data processing tools is typically required, with relevant certifications being a plus. Strong problem-solving, critical thinking, and communication skills help you translate complex data insights into actionable solutions and work effectively in teams. These abilities ensure accurate model development, effective deployment, and successful collaboration on data-driven projects.

What are ML Engineers?

ML Engineers, or Machine Learning Engineers, are professionals who design, build, and deploy machine learning models into production systems. They bridge the gap between data science and software engineering, ensuring that machine learning solutions are scalable, reliable, and efficient. ML Engineers work with large datasets, develop algorithms, and optimize models for performance. They also collaborate with data scientists, software developers, and business stakeholders to solve real-world problems using artificial intelligence.

What is the difference between Ml Engineer vs Data Scientist?

AspectML EngineerData Scientist
Required CredentialsBachelor's or Master's in CS, Data Science, or related fields; knowledge of ML frameworksBachelor's or Master's in Statistics, Data Science, or related fields; strong analytical skills
Work EnvironmentDevelops, deploys, and maintains ML models in production systemsAnalyzes data, builds models, and provides insights for decision-making
Employer & Industry UsageTech companies, startups, and enterprises deploying ML solutionsResearch institutions, tech firms, and industries relying on data analysis

While both roles involve working with data and machine learning, ML Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights to inform business decisions. The roles often overlap but differ in their core responsibilities and focus areas.

What are some common challenges Machine Learning Engineers face when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring models remain accurate over time as data changes (known as data drift), optimizing models for speed and scalability, and integrating models seamlessly with existing software systems. Additionally, maintaining model performance in real-world environments can require continuous monitoring, retraining, and close collaboration with data engineers and DevOps teams. Addressing these challenges typically involves robust testing, using automated pipelines, and staying up-to-date with the latest MLOps best practices.

Are ML engineers still in demand?

Yes, ML engineers are in high demand due to the growing adoption of machine learning and AI across industries. They are sought after for their skills in data modeling, programming, and tools like TensorFlow and PyTorch, with job opportunities expected to remain strong as organizations continue to leverage AI technologies.
What are popular job titles related to Ml Engineer jobs in Spring, TX? For Ml Engineer jobs in Spring, TX, the most frequently searched job titles are:
What job categories do people searching Ml Engineer jobs in Spring, TX look for? The top searched job categories for Ml Engineer jobs in Spring, TX are:
What cities near Spring, TX are hiring for Ml Engineer jobs? Cities near Spring, TX with the most Ml Engineer job openings:
Infographic showing various Ml Engineer job openings in Spring, TX as of July 2026, with employment types broken down into 91% Full Time, 7% Part Time, and 2% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $79,363 per year, or $38.2 per hour.
Principal AI/ML Software Engineer

Principal AI/ML Software Engineer

Hexagon AB

Houston, TX โ€ข On-site

$124K - $167K/yr

Full-time

Posted 6 days ago


Job description

Principal AI/ML Software Engineer
Job Location (Short): Houston, Texas-USA | Madison, Alabama-USA
Workplace Type: Remote
Req Id: 2909
Responsibilities
Position Overview
We are seeking a motivated AI/ML Engineer to build reliable, scalable systems and Generative AI and Agentic AI features, and build and deploy 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.
Key Responsibilities
โ€ข Build and deploy Generative AI features using foundation models (AWS Bedrock, OpenAI, Anthropic Claude) and inference pipelines with optimization of latency and cost
โ€ข Design agentic AI systems that autonomously handle compliance workflows, document review, regulatory mapping, and multi-step reasoning tasks
โ€ข Integrate comprehensive LLM evaluation frameworks with development and production systems
โ€ข Build and operate end-to-end MLOps pipelines, deployment systems, monitoring, and rollbacks workflows
โ€ข Implement explainability frameworks (SHAP/LIME) and monitoring dashboards ensuring transparency and regulatory adherence
โ€ข Collaborate with cross-functional teams to translate business needs into ML solutions and communicate insights to stakeholders
#LI-PB1 LI-Remote
Education / Qualifications
Technical Skills
โ€ข Python (5+ years): Production-level experience with Pandas, NumPy, scikit-learn, XGBoost, TensorFlow/PyTorch, Hugging Face Transformers, FastAPI/Flask, MLflow, and pytest
โ€ข SQL: Advanced proficiency with complex queries, window functions, and optimization
โ€ข Machine Learning & NLP: Strong foundation in supervised/unsupervised learning, deep learning, document understanding, text classification, and semantic analysis
โ€ข Generative AI & LLMs: Hands-on experience with foundation models (GPT, Claude, Llama), prompt engineering, RAG architectures, and vector databases (Pinecone, Weaviate, Chroma)
โ€ข MLOps & ModelOps: End-to-end experience with ML pipelines, model versioning, feature stores, drift detection, CI/CD for ML, and Docker containerization
โ€ข LLM Evaluation: Experience with evaluation frameworks (RAGAS, DeepEval), custom metrics, benchmark datasets, and human-in-the-loop validation
โ€ข Cloud & AWS: Experience with AWS services including SageMaker, Bedrock, S3, Lambda, EC2, and CloudWatch
โ€ข Statistics & Experimentation: Strong foundation in statistics, A/B testing, causal inference, and experimental design
โ€ข Visualization: Proficiency with Tableau, Power BI, or Python visualization libraries
Experience & Education
โ€ข 5+ years in data science, ML engineering, or related roles
โ€ข 3+ years building NLP/generative AI applications and implementing MLOps in production
โ€ข Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or related field
โ€ข Track record of deploying ML systems processing large-scale datasets with proper monitoring and governance
Preferred Qualifications
โ€ข Experience with agentic AI frameworks (LangGraph, LangChain, AutoGen, CrewAI) ?
โ€ข Knowledge of Life Sciences/regulated industries (FDA, EMA, ISO, GxP) and compliance management systems
โ€ข Familiarity with big data tools (Spark, Databricks, Snowflake), orchestration (Airflow, Kubeflow), and monitoring tools (Datadog, Prometheus)
โ€ข Experience with LLM fine-tuning, document processing libraries, multi-modal AI, or distributed training
โ€ข Understanding of ML governance, bias detection, model risk management, and data privacy regulations (GDPR, CCPA, HIPAA)
โ€ข Experience working in agile environments with Jira
โ€ข AWS ML certifications or similar credentials
Key Competencies
โ€ข Strong communication skills explaining complex models to technical and nontechnical audiences
โ€ข Ability to work independently and collaboratively in fast-paced environments
โ€ข Proven ability to convert POCs into production-grade solutions
โ€ข Understanding of ethical AI and building trustworthy, explainable systems for regulated environments
What You'll Build
โ€ข LLM evaluation frameworks ensuring 95%+ accuracy for compliance-critical features
โ€ข Prompts for LLMs to achieve specific, high-quality outcomes
โ€ข Agentic AI systems autonomously handling document review and compliance workflows
โ€ข GenAI document understanding features processing millions of regulatory documents
โ€ข Predictive models identifying compliance risks before they occur
โ€ข Real-time semantic search and explainable ML systems meeting regulatory requirements
โ€ข Production MLOps pipelines supporting dozens of models with automated monitoring and retraining
Growth Opportunities
โ€ข Drive adoption of emerging AI technologies and establish best practices
โ€ข Mentor ML engineers
โ€ข Shape AI/ML roadmap and establish center of excellence for compliance AI
โ€ข Collaborate with product leadership on long-term vision for AI-powered compliance
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 more than 7,000 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