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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer (AI/ML) - **Company:** MIRYALA FAMILY HOLDINGS LLC - **Location:** Dallas, TX, United States (Remote available) - **Experience:** Expert - **Contract:** Contract - **Skills:** Artificial Intelligence, Application Integration Architecture, Computer Vision, BigQuery, Cloud Computing, Cloud Storage, Computer Programming, Continuous Integration, Information Engineering, Data Transformation, Database Queries, Software Debugging, Distributed Computing Environment, Data Flow Control, Identity and Access Management, Python (Programming Language), Machine Learning, NumPy, Performance Tuning, Tensorflow, Cloudera, Azure Machine Learning, Software Construction, Software Engineering, Data Streaming, Unstructured Data, Google Cloud, Feature Engineering, Pytorch, System Availability, Apache Spark, Apigee, Git, Pandas, Containerization, AI Platforms, Scikit Learn, Kubernetes, Information Technology, Dask, Machine Learning Operations, Multiaccess Edge Computing, Restful APIs, Looker Analytics, Software Version Control, Data Pipelines, Docker, Microservices - **Published:** August 29, 2026 - **Apply:** https://www.careerjet.com/jobad/us0f276d352ef3a88b17c0069aad61cf2c ## About the Role Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field. Experience: 5+ years of professional experience in software engineering with a focus on AI/ML. Programming: Strong proficiency in Python with expertise in libraries such as TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy. Cloud (GCP): Deep hands-on experience with Google Cloud Platform, including: Vertex AI / AI Platform BigQuery, Cloud Storage, Dataflow Cloud Functions / Cloud Run IAM and security best practices MLOps: Experience with MLflow, Kubeflow, or similar tools for model lifecycle management. Data Skills: Strong SQL skills and experience working with large-scale structured and unstructured datasets. Software Engineering: Solid understanding of software development principles, version control (Git), CI/CD, containerization (Docker), and orchestration (Kubernetes). Problem-Solving: Ability to debug complex systems, optimize algorithms, and propose innovative solutions. Preferred Qualifications: Experience with NLP, computer vision, or time-series forecasting. Familiarity with additional GCP services (e.g., Dataproc, Looker, or Apigee). Knowledge of distributed computing frameworks (e.g., Spark, Dask). Prior experience in a contract or consulting environment. Relevant certifications (e.g., Google Professional ML Engineer). ## Description We are seeking a highly skilled Software Engineer (AI/ML) to join our dynamic team in Atlanta, GA. In this on-site contract role, you will design, develop, and deploy cutting-edge machine learning models and AI-driven solutions. You will work closely with data scientists, software engineers, and product stakeholders to build scalable, production-ready systems that solve complex business problems. The ideal candidate is deeply proficient in Python, has hands-on experience with Google Cloud Platform (GCP) AI/ML services, and possesses a strong foundation in software engineering best practices. Key Responsibilities: Model Development & Deployment: Design, build, train, and deploy machine learning models using Python and GCP's AI/ML stack (e.g., Vertex AI, AI Platform, AutoML). Data Engineering & Pipelines: Develop and maintain scalable data pipelines for preprocessing, feature engineering, and model training using GCP services (BigQuery, Dataflow, Pub/Sub). MLOps & Automation: Implement CI/CD pipelines for model versioning, testing, monitoring, and retraining to ensure high availability and performance in production. API & Integration: Build RESTful APIs and microservices to serve model predictions and integrate AI capabilities into existing applications and workflows. Performance Optimization: Optimize model inference latency, scalability, and cost-efficiency on GCP infrastructure. Collaboration: Partner with data scientists to operationalize research prototypes and with product teams to translate business requirements into technical AI solutions. Documentation & Best Practices: Maintain clear documentation for model architecture, data flows, and deployment processes, adhering to software engineering and security standards. ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Vectorize all the things! 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