Senior AI/ML Engineer -Lead MLOps Engineer

Role: Senior AI/ML Engineer / Lead MLOps Engineer

Title: Senior AI/ML Engineer (MLOps & Computer Vision) / Lead MLOps Engineer
Location: Austin, TX

Total Required Experience in Years: 15%2B Years

Mode of Work: Hybrid (3 Days Onsite / 2 Days Remote) Austin, TX Local Candidates Only

Job Description:

a Senior AI/ML Engineer / Lead MLOps Engineer to advance enterprise AI initiatives and transform existing proof-of-concept solutions into scalable, production-grade applications supporting transportation engineering operations.

The selected candidate will lead the design, deployment, automation, monitoring, and optimization of AI/ML solutions focused on transportation infrastructure, roadway asset detection, engineering plan review automation, digital twins, and intelligent decision-support systems.

This role requires deep expertise across MLOps, Computer Vision, Large Language Models (LLMs), cloud-native AI platforms, GIS analytics, and enterprise-scale AI deployment.

Key Responsibilities:

• Design, build, deploy, and maintain enterprise-scale AI/ML solutions.

• Convert AI proof-of-concept applications into secure, scalable production systems.

• Build MLOps pipelines supporting model training, deployment, monitoring, governance, and lifecycle management.

• Develop Computer Vision solutions for transportation infrastructure and roadway asset detection.

• Build and optimize LLM-based applications using RAG, prompt engineering, and fine-tuning methodologies.

• Implement AI-powered engineering workflow automation and digital delivery solutions.

• Develop scalable web applications integrating AI/ML services.

• Design distributed training architectures for large-scale model development.

• Implement feature stores, model registries, and AI governance frameworks.

• Collaborate with transportation engineers, GIS analysts, architects, and business stakeholders.

• Support digital twin initiatives leveraging GIS and Unreal Engine technologies.

• Optimize model performance using quantization, pruning, and knowledge distillation techniques.

• Establish enterprise AI operational standards and best practices.

Required Skills:

AI/ML & MLOps

• 15%2B years of overall IT experience.

• 8%2B years of Machine Learning and AI engineering experience.

• 5%2B years of production MLOps experience.

• Experience deploying AI systems serving real-world users.

• Strong expertise with:

  • MLflow

  • Kubeflow

  • Airflow

  • Weights & Biases

  • Feature Stores (Feast, Tecton)

Multi-Cloud AI Platforms

• Experience with:

  • AWS SageMaker

  • AWS Bedrock

  • Azure AI Services

  • Azure OpenAI

  • Google Vertex AI

  • OCI AI Services

• Multi-cloud AI architecture and deployment expertise.

DevOps & Infrastructure

• Kubernetes

• Docker

• CI/CD Pipelines

• Azure DevOps

• GitHub Actions

• Jenkins

• Ansible

• Infrastructure Automation

Programming

• 5%2B years of production Python development.

• Bash scripting.

• PowerShell scripting.

LLMs & Generative AI

• GPT Models

• Hugging Face

• Ollama

• Transformers

• RAG Architectures

• Prompt Engineering

• Fine-Tuning

• Agentic AI Frameworks

Computer Vision

• PyTorch

• TensorFlow

• OpenCV

• YOLO

• Object Detection

• Semantic Segmentation

• Real-Time Inference Systems

Data Engineering

• SQL

• PostgreSQL

• MySQL

• NoSQL Databases

• Vector Databases

• Distributed Data Processing

Advanced AI Areas

• Recommender Systems

• Time-Series Forecasting

• Anomaly Detection

• Distributed Training

• Multi-GPU Training

• Model Optimization

Domain Experience (Mandatory)

• Transportation Industry

• Logistics

• Smart Cities

• Intelligent Infrastructure

• Public Sector Transportation Systems

GIS & Spatial Analytics (Mandatory)

• Strong experience with Geospatial Information Systems (GIS).

• Spatial Data Analysis.

• Geospatial Modeling.

• Location Intelligence.

• Infrastructure Asset Mapping.

Preferred Skills:

• Unreal Engine Digital Twins.

• Google Maps Platform.

• Cesium API.

• Polygonflow Dash.

• Transportation Asset Management Systems.

• Smart Mobility Solutions.

• Connected Infrastructure Platforms.

Qualifications:

• Bachelor\'s Degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field.

• Master\'s Degree preferred.

Certifications:

• AWS Machine Learning Specialty preferred.

• Azure AI Engineer Associate preferred.

• Google Professional Machine Learning Engineer preferred.

• Kubernetes Certification preferred.

• Databricks Machine Learning Certification preferred.

Additional Information:

• Location: Austin, Texas

• Hybrid Schedule: 3 Days Onsite / 2 Days Remote

• Candidates must reside within 50 miles of Austin, TX.

• Face-to-Face Interview may be required.

• Visa Accepted: USC, GC, H1B, H4-EAD

• Transportation, Logistics, Smart City, or GIS domain experience is mandatory.

• Strong GIS and Spatial Analytics experience is mandatory.

• Candidates with less than 15 years of IT experience will not be considered.