- Globenet Consulting Corp
- BELLEVUE, WA
- Full-Time
- 67 days ago
- $130,000 – $155,000
AI/ML Technical Lead.
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AI/ML Technical Lead: our view in 3 lines...
- The Role:This role is for an AI and machine learning lead who builds production AI solutions and large language model applications.
- The Person:The person will design, train, test, and deploy AI and machine learning models, build scalable ML pipelines, integrate AI capabilities into applications, and provide technical direction, code reviews, and mentoring.
- Requirements:The ideal candidate has strong Python skills, experience with PyTorch, TensorFlow, Scikit-learn, and XGBoost, and familiarity with Generative AI, LLMs, NLP, computer vision, and deep learning.
About the role
Benefits:
- Competitive salary
- Opportunity for advancement
- Training & development
Role: AI/ML Technical Lead
Location: Fort Belvoir, VA 22060
Location: Fort Belvoir, VA 22060
Let’s Create Our Future Together at The AES Group!
Position Overview
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.
Required Qualifications
- 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.
Preferred Qualifications
- 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
