AI/ML Technical Lead

Globenet Consulting Corp•BELLEVUE, WA

20h•$130,000 - $155,000•Onsite

About The Position

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.

Requirements

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.

Nice To Haves

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.

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.