AI/ML Full Stack Engineer - Locals Only

Title: AI/ML Full Stack Engineer
Duration: Long Term
Location: Washington, DC 20433
Hybrid Onsite: 4 Days per week onsite from Day 1

Note: To succeed in this position, you need a strong technical foundation and experience with both the AI and software development lifecycles.

Role and responsibilities:

• Developing AI models: Building and fine-tuning machine learning models, including specialized ones like large language models (LLMs), to solve development challenges.
• Engineering cloud applications: Designing and building cloud-native data and AI tools and ensuring they are scalable, secure, and performant. The client frequently uses cloud platforms like Azure, Google Cloud Platform, or AWS.
• Building user interfaces: Developing the front-end user experience, including interactive data and knowledge dashboards.
• Managing data infrastructure: Handling large, complex datasets, including data sourcing, cleaning, and preparation for model training.
• Implementing best practices: Coordinating the technical work of data teams by conducting code reviews, implementing MLOps strategies, and ensuring ethical AI use.
• Working with stakeholders: Collaborating with internal teams and external partners to gather requirements and present AI-driven solutions.

Required skills and qualifications:

• Education: A master's degree or higher in a relevant field, such as Computer Science, Data Science, or Artificial Intelligence, is typically required.
• Experience: A minimum of 7-10 years of professional experience in a related role is often requested. This should include hands-on experience in AI engineering and cloud-native application development.
• Programming languages: Proficiency in Python is essential, along with experience using AI/ML frameworks like TensorFlow, PyTorch, or Scikit-learn. Strong skills in JavaScript/TypeScript and front-end frameworks like React are also necessary for full-stack roles.
• Cloud platforms: Expertise in cloud platforms (Azure, AWS, or Google Cloud Platform) and related services is critical for deploying and managing AI solutions.
• Databases: Knowledge of both relational and NoSQL databases is required.
• Other technical skills: Experience with containerization (Docker, Kubernetes), version control (Git), and CI/CD pipelines is a major asset.
• Soft skills: Excellent communication, problem-solving, and collaboration skills are vital for working in interdisciplinary and multicultural teams.