AI Solutions Engineer

Job Description:
One of our top clients is hiring a key resource to support the AI/ML modernization and transformation of their Retail and Delivery Technology systems/applications. This role is critical in designing, developing, and deploying Machine Learning models and intelligent automation solutions supporting nationwide postal operations—including retail point-of-sale (POS) systems, money order processes, mail tracking, and delivery optimization. Huge opportunity to apply advanced AI capabilities to one of the largest retail and logistics environments in the federal space!

Requirements:
• 7+ years of experience designing, developing, and deploying AI/ML models and data-driven automation solutions using Python programming
• Including recent customer-facing experience (external facing applications/models)
• 4+ years hands-on developing, training, and optimizing machine learning models using frameworks such as TensorFlow, PyTorch, or scikit-learn for real-world data applications
• Extensive experience deploying AI/ML solutions integrated into cloud platforms (AWS, Azure, GCP)
• Prompt Engineering experience – refining/integrating GenAI LLMs solutions (large language models including ChatGPT, Gemini, Anthropic, etc.)
• Agile, Enterprise level environment experience – ability to work across technical & non-technical large-scale teams (business stakeholders, data engineers, developers, etc.)
• Must be able to obtain Public Trust clearance (government level background check w/ fingerprinting)
• B.S. Degree

Nice to have:
• Federal/Government agency experience
• Strong data visualization experience (Tableau, Power BI) and version control systems (Git)
• Extensive experience with deep learning, NLP, and computer vision AI techniques

Responsibilities:
• Design and implement end-to-end automation workflows that utilize generative AI and other models to streamline and reduce manual tasks.
• Build and maintain robust data and AI pipelines for information extraction and retrieval-augmented generation (RAG).
• Integrate AI capabilities into existing business applications and processes to enhance functionality and user interaction.
• Optimize and fine-tune existing AI models to enhance their accuracy, efficiency, and overall performance in multi-cloud environments
• Conduct continuous monitoring and evaluation of deployed AI systems to ensure sustained performance and identify opportunities for further improvement.
• Analyze complex datasets using AI and machine learning techniques to uncover trends, patterns, and actionable insights that align with business objectives.
• Develop custom data models and algorithms to perform predictive modeling that optimizes business outcomes such as revenue generation or customer experiences.