AI/ML Engineer

Title: AI/ML Full Stack Engineer Duration: 9 Months - Long Term Location: Washington, DC 20433 Hybrid Onsite: 4 Days per week onsite from Day1 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 bank frequently uses cloud platforms like Azure, GCP, 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: To succeed in this position, you need a strong technical foundation and experience with both the AI and software development lifecycles. • 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 GCP) 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. “Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.”