What are the responsibilities and job description for the Sr. AI/ML Engineer position at Masterapp Labs?
Job Title: Sr. AI/ML Engineer
Location: Austin, TX, (Hybrid)- On Site and Telework
Position Type: Contract
Interview Mode: MS Teams & In-person
Key Responsibilities:
- Design, develop, and maintain software services supporting engineering workflows
- Implement model ingestion pipelines and automate quantity extraction processes
- Build and enhance plan conformance validation systems
- Develop and maintain CI/CD pipelines to support automated integration and deployment
- Extend AI-based proof-of-concept solutions into scalable, production-ready applications
- Develop secure, responsive, and user-friendly web applications
- Integrate AI/ML models into enterprise systems and engineering workflows
- Ensure compliance with procurement and organizational standards using PEPS
- Collaborate with cross-functional teams including engineers, analysts, and stakeholders
- Optimize application performance, scalability, and reliability
II. CANDIDATE SKILLS AND QUALIFICATIONS
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Minimum Requirements:
Candidates that do not meet or exceed the minimum stated requirements (skills/experience) will be displayed to customers but may not be chosen for this opportunity.
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Years
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Required/Preferred
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Experience
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8
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Required
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Cloud Platforms: Experience with AWS, Azure, Google Cloud Platform, or OCI for deploying and managing ML workloads. We leverage AI/ML tools across all major cloud providers (Azure AI, AWS SageMaker/Bedrock, Google Cloud Platform Vertex AI, OCI AI Services).
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8
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Required
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DevOps: Ansible, CI/CD, Docker and Kubernetes experience.
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8
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Required
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Databases: SQL (PostgreSQL, MySQL) and NoSQL/vector databases.
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8
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Required
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Scripting: Proficient in both Bash and PowerShell for automation.
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8
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Required
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CI/CD Experience: Azure DevOps, GitHub Actions, Jenkins, or similar automation pipelines.
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3
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Required
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Python: 3-5 years production experience, this is your primary language.
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3
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Required
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NLP/LLMs: Experience with transformers (BERT, GPT, T5), RAG systems, fine-tuning, prompt engineering, or building LLM applications.
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3
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Required
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Time Series: Forecasting models, anomaly detection, sequential data modeling, or real-time monitoring systems.
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3
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Required
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Recommender Systems: Collaborative filtering, ranking models, personalization engines, or content recommendations.
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3
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Required
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MLOps Tools: Production experience with MLflow, Weights & Biases, Kubeflow, Airflow, or similar platforms.
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3
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Required
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Distributed Training: Large-scale model training, multi-GPU/multi-node setups, efficient data parallelism.
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3
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Required
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Computer Vision: Production CV experience with PyTorch/TensorFlow, OpenCV, YOLO, object detection, segmentation, or real-time inference.
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3
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Required
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Feature stores (Feast, Tecton) or advanced feature engineering.
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3
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Required
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Model optimization: quantization, pruning, knowledge distillation.
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3
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Required
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LLM Models: Ollama, Huggingface, or other non-frontier models
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2
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Required
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AI/ML Production: Built and deployed 2-3 ML models serving real users, not just experiments.
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1
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Preferred
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Experience with Geospatial Information Systems (GIS) and analyzing spatial data.
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1
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Preferred
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Prior experience in the transportation, logistics, or smart city sectors.
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1
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Preferred
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Background in Computer Vision (object detection, image segmentation) applied to infrastructure or vehicular data.
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1
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Preferred
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Familiarity with public sector data compliance, security, and governance standards.
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1
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Preferred
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Experience with the Unreal gaming engine and real world digital twinning
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1
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Preferred
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Experience with Google Maps Cesium API
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1
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Preferred
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Experience with Polygonflow Dash and its capabilities
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