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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI/ML Technical Lead - **Company:** Globenet Consulting Corp - **Location:** Bellevue, WA, United States - **Experience:** Expert - **Salary:** $130,000.0 - $155,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Computer Vision, Microsoft Azure, Big Data, Cloud Database, Code Review, Continuous Integration, R (Programming Language), Python (Programming Language), Machine Learning, Natural Language Processing, NumPy, Recommender Systems, Tensorflow, Software Deployment, Software Engineering, SQL Databases, Unstructured Data, Data Processing, Google Cloud, Feature Engineering, Pytorch, Large Language Models, Snowflake, Apache Spark, Deep Learning, Model Validation, Generative AI, Keras, Pandas, Scikit Learn, Kubernetes, Information Technology, HuggingFace, Xgboost, Machine Learning Operations, Docker, Databricks - **Published:** July 26, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=e0b3ae67340571e6 ## About the Role * Active Secret security clearance or higher. * Bachelor's degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, Engineering, or a related field. * Three or more years of experience in AI, machine learning, data science, or software engineering. * Strong Python programming skills. * Experience with PyTorch, TensorFlow, Scikit-learn, Keras, XGBoost, or similar frameworks. * Experience developing and deploying production machine learning models. * Strong knowledge of algorithms, feature engineering, statistical analysis, and model evaluation. * Experience processing large datasets using modern data tools. * Familiarity with APIs, cloud platforms, and software development practices. * Ability to communicate complex technical concepts to technical and non-technical stakeholders., * Master's degree or PhD in a related field. * Experience with Generative AI, LLMs, NLP, computer vision, or deep learning. * Experience with Ask Sage, Hugging Face, LangChain, OpenAI APIs, Azure AI, AWS SageMaker, or Google Vertex AI. * Experience with MLflow, Kubeflow, Airflow, Docker, Kubernetes, and CI/CD pipelines. * Experience with SQL, Spark, Databricks, Snowflake, or cloud data warehouses. * Knowledge of AI governance, ethics, bias testing, security, and data privacy standards. * Experience deploying AI solutions in enterprise environments. Technical Skills * Languages: Python, SQL, and R * ML Frameworks: PyTorch, TensorFlow, Scikit-learn, and XGBoost * Cloud Platforms: AWS, Microsoft Azure, or Google Cloud * MLOps: Docker, Kubernetes, MLflow, Airflow, and CI/CD * Data Tools: Pandas, NumPy, Spark, Snowflake, and Databricks * AI/LLM Tools: Ask Sage, Hugging Face, LangChain, OpenAI, and vector databases ## Description We are seeking an AI/ML Technical Lead to design, build, and deploy scalable machine learning models and AI-powered solutions. This role will collaborate with engineering, product, data, and business teams to transform complex data into practical, measurable solutions. The ideal candidate has strong technical leadership, problem-solving skills, production AI/ML experience, and expertise in Large Language Models. Key Responsibilities * Lead the design, development, training, testing, and deployment of AI and machine learning models. * Build scalable ML pipelines for data processing, model training, validation, and production deployment. * Work with structured and unstructured data, including text, images, documents, and large datasets. * Collaborate with data engineers, software engineers, and product teams to integrate AI/ML capabilities into applications. * Evaluate and improve model accuracy, efficiency, reliability, scalability, and performance. * Develop predictive models, recommendation systems, NLP tools, automation workflows, and generative AI solutions. * Research and apply modern AI/ML tools, techniques, architectures, and best practices. * Monitor deployed models and address model drift, bias, data quality, and performance issues. * Document model architecture, assumptions, limitations, metrics, and technical decisions. * Promote responsible AI practices related to security, privacy, fairness, governance, and compliance. * Provide technical direction, code reviews, mentoring, and implementation guidance to engineering teams. ## Related Videos - [How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge](https://www.wearedevelopers.com/videos/1328-how-to-avoid-llm-pitfalls-mete-atamel-and-guillaume-laforge) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Vectorize all the things! 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