AI/ML Engineer

STONEGATE TECHNOLOGIES LLC; Austin (TX); Hybrid; USD 120,000 - 180,000; Full time

Job summary

STONEGATE TECHNOLOGIES LLC is seeking an experienced AI/ML Engineer to join our hybrid team based in Austin, TX, with some onsite presence required and occasional collaboration with our Cupertino group. The role emphasizes production-grade ML systems, model deployment, and data engineering in close partnership with Data Scientists.

You will design scalable ML pipelines, participate in model experimentation, and implement MLOps practices across cloud environments.

Qualifications

• 5+ years of AI/ML engineering experience.
• Collaboration with Data Scientists on model development and productionization.
• Strong hands-on Python with ML libraries (NumPy, Pandas, Scikit-Learn, PyTorch, TensorFlow).
• Experience with ML pipelines, training, validation, deployment.
• Data processing with Spark/Hadoop.
• Cloud experience: AWS or Google Cloud Platform.
• End-to-end ML workflows and MLOps, CI/CD.
• SQL and NoSQL databases.

Responsibilities

• Work with Data Scientist teams to build, optimize, and deploy ML models.
• Develop scalable ML pipelines, automation frameworks, and data workflows.
• Support model experimentation, tuning, and performance optimization.
• Deploy ML solutions using cloud-native MLOps best practices.
• Build tools for model monitoring, drift detection, and reliability.
• Collaborate with engineering, analytics, and product teams.
• Troubleshoot complex ML pipeline issues and drive root-cause analysis.
• Maintain documentation, runbooks, and engineering standards.
• Ensure production readiness and continuous improvement of ML systems.

Skills

AI/ML engineering

Data Scientist collaboration

Python stack

ML pipelines

Data processing

Cloud AWS/GCP

MLOps/CI/CD

SQL/NoSQL

Onsite availability

Communication skills

Tools

Docker

Kubernetes

Job description

Title: AI/ML Engineer

Location: Austin, TX / Cupertino, CA

Schedule: Hybrid (Tue Thu onsite, Mon & Fri remote)

Contract: Longterm

Note: Local candidates strongly preferred

Start: Day 1 Onsite (No exceptions)

Position Summary

We are seeking an experienced AI/ML Engineer with strong handson experience in machine learning development, model deployment, and data engineering. The ideal candidate has worked closely with Data Scientist teams, supported model experimentation, and built productiongrade ML systems.

MustHave Skills

• 5 10+ years of experience in AI/ML engineering
• Proven experience collaborating directly with Data Scientists on model development, feature engineering, and productionization
• Strong handson experience with:
• Python (NumPy, Pandas, ScikitLearn, PyTorch, TensorFlow)
• ML pipelines, training, validation, deployment
• Data processing (Spark, Hadoop, distributed systems)
• Cloud experience: AWS or Google Cloud Platform
• Experience building endtoend ML workflows
• Strong understanding of MLOps, CI/CD, automation, model versioning
• Experience with SQL and NoSQL databases
• Ability to work onsite 3 days/week (Tue Thu)
• Excellent communication and crossfunctional collaboration skills

NicetoHave Skills

• Experience with LLMs, NLP, embeddings, vector databases
• Familiarity with feature stores, model registries, ML observability tools
• Experience with Docker, Kubernetes, microservices
• Experience supporting Data Science experimentation and scaling models to production
• Background in data governance, privacy, and compliance

Key Responsibilities

• Work closely with Data Scientist teams to build, optimize, and deploy ML models
• Develop scalable ML pipelines, automation frameworks, and data workflows
• Support model experimentation, tuning, and performance optimization
• Deploy ML solutions using cloudnative MLOps best practices
• Build tools for model monitoring, drift detection, and reliability
• Collaborate with engineering, analytics, and product teams
• Troubleshoot complex ML pipeline issues and drive rootcause analysis
• Maintain documentation, runbooks, and engineering standards
• Ensure production readiness and continuous improvement of ML systems