AI Software Engineer, Senior

Cayuse Holdings•Cedar Park, TX

1d•$116,480 - $166,400•Onsite

About The Position

The Senior AI Software Engineer will be responsible for advancing enterprise AI initiatives by extending existing proof‑of‑concept (POC) solutions into scalable, production‑ready web applications that directly support complex engineering workflows. This role focuses on transforming early‑stage AI models and prototypes—such as those supporting plan review automation, asset detection, digital delivery, and other domain‑specific use cases—into fully integrated applications accessible through secure, user‑friendly web interfaces across the organization. The Senior AI Software Engineer will concentrate on engineering‑related software services, including model ingestion, automated quantity extraction, plan conformance checks, and CI/CD automation. The position requires close collaboration with cross‑functional teams (engineering, product, infrastructure, and security) to ensure solutions are robust, compliant, and aligned with enterprise standards and regulatory requirements. This position aligns with Cayuse’s core values of Innovation, Excellence, Collaboration, Adaptability, and Integrity by fostering technical solutions that meet customer needs, promoting teamwork, and prioritizing quality in deliverables.

Requirements

8+ years of professional software engineering experience, with substantial work in AI/ML and cloud‑native development
Experience with at least one major cloud platform (AWS, Azure, GCP, or OCI) for deploying and managing ML workloads
Hands‑on experience with cloud AI/ML services such as Azure AI, AWS SageMaker/Bedrock, GCP Vertex AI, or OCI AI Services
Strong DevOps background, including: Ansible for configuration management and automation, Docker for containerization, Kubernetes for container orchestration, CI/CD best practices for automated build, test, and deployment
Proficiency with relational and non‑relational databases, including: SQL (PostgreSQL, MySQL), NoSQL and vector databases for similarity search and embedding‑based retrieval
Strong scripting skills in both: Bash, PowerShell
Proven experience designing and maintaining CI/CD pipelines using: Azure DevOps, GitHub Actions, Jenkins, Or similar automation tools
3–5+ years of production‑level Python development (primary implementation language)
3+ years of experience with NLP and LLMs, including: Transformer models (BERT, GPT, T5, etc.), RAG (Retrieval‑Augmented Generation) systems, Fine‑tuning and prompt engineering, Building LLM‑based applications
3+ years of experience with time‑series data, including: Forecasting models, Anomaly detection, Sequential data modeling, Real‑time monitoring systems
3+ years of experience building recommender systems, such as: Collaborative filtering, Ranking models, Personalization engines, Content recommendation pipelines
Production experience with MLOps tools and platforms, such as: MLflow, Weights & Biases, Kubeflow, Airflow, Or similar systems for orchestration, tracking, and model lifecycle management
Experience with distributed training, including: Large‑scale model training, Multi‑GPU and/or multi‑node setups, Data/model parallelism and performance optimization
Production computer vision experience using: PyTorch and/or TensorFlow, OpenCV, YOLO or similar frameworks for object detection and segmentation, Real‑time inference and deployment workflows
Experience with feature stores (e.g., Feast, Tecton) and/or advanced feature engineering techniques
Hands‑on experience with model optimization techniques: Quantization, Pruning, Knowledge distillation
Experience working with LLM ecosystems such as: Ollama, Hugging Face, Other non‑frontier / open‑weight models
Demonstrated AI/ML production track record: Built and deployed at least 2–3+ ML models serving real users (beyond experimental or research‑only projects)
Must be able to pass a background check. May require additional background checks as required by projects and/or clients at any time during employment.
Exceptional interpersonal skills with the ability to communicate in a clear, professional, and articulate manner.
Exceptional verbal and written communication skills.
Excellent organizational, analytical, and problem-solving skills with high-level attention to detail.
Proven ability to multitask and prioritize in a fast past environment with changing priorities; adaptable to change and a quick learner.
Must be self-motivated and able to work well independently as well as on a multi-functional team.
Ability to handle sensitive and confidential information appropriately
Proficient in MS Office, Word, Outlook, PowerPoint, and Excel.

Nice To Haves

1+ year of experience with Geospatial Information Systems (GIS) and analyzing or modeling spatial data
Prior experience in one or more of the following domains: Transportation, Logistics, Smart city or urban infrastructure
Background applying computer vision to infrastructure or vehicular data, including: Object detection, Image segmentation, Video or sensor data analysis
Familiarity with public sector data compliance, security, and governance, such as: Data classification and handling, Access control and audit requirements, Regulatory and policy constraints for government data
Experience with Unreal Engine in the context of: Real‑world digital twinning, Simulation or immersive visualization of physical environments
Experience integrating or building solutions with: Google Maps APIs, Cesium or similar 3D mapping/geospatial visualization platforms
Experience with Polygonflow Dash and its capabilities for: 3D workflows, Visualization pipelines, Automation of complex modeling or simulation tasks

Responsibilities

Design, develop, and maintain production‑grade AI/ML services and web applications that extend existing POC solutions into scalable, secure, and reliable enterprise platforms.
Implement and optimize AI/ML workflows for: Model ingestion and lifecycle management, Automated quantity extraction from plans and documents, Plan conformance and rules‑based checks, Computer vision–based asset detection and inspection, NLP/LLM‑based plan review automation and document analysis
Build secure, user‑friendly web interfaces and APIs that enable engineering and business users to leverage AI capabilities within their day‑to‑day workflows.
Architect, implement, and manage CI/CD pipelines to support rapid, reliable deployment of AI/ML models and related services.
Deploy and manage AI/ML workloads across one or more major cloud platforms (AWS, Azure, GCP, OCI), leveraging native AI/ML services as appropriate.
Implement MLOps best practices, including experiment tracking, model registry, feature stores, monitoring, and automated retraining where appropriate.
Optimize model performance and cost through techniques such as quantization, pruning, distillation, and efficient distributed training.
Integrate and operationalize LLM and NLP solutions (e.g., transformers, RAG systems) to support text understanding, summarization, Q&A, and other intelligent automation use cases.
Collaborate with data engineers, cloud engineers, and domain experts to design robust data pipelines and architectures for AI/ML workloads, including time‑series, image/video, and text data.
Ensure that all solutions adhere to security, compliance, and governance standards, especially when working with sensitive or regulated data.
Provide technical leadership, mentorship, and guidance to junior engineers and peers, promoting best practices in AI/ML engineering, DevOps, and software craftsmanship.
Produce high‑quality technical documentation, including architecture diagrams, API specifications, deployment runbooks, and user guides.
Participate in technical planning, backlog grooming, and estimation; contribute to roadmap development for AI/ML capabilities.
Other duties as assigned