AI/ML Solutions Architect

AI/ML Solutions Architect

The AI/ML Solutions Architect will be instrumental in designing and implementing end-to-end artificial intelligence and machine learning solutions for a key Randstad client in the DC area. This role requires an expert-level blend of advanced AI/ML model development (including Generative AI/LLMs, deep learning, and traditional ML), modern software engineering practices, and robust MLOps principles. The Architect will drive platform adoption using Databricks, ensure models are securely deployed via cloud platforms (AWS/Azure) using Docker/Kubernetes and FastAPI, and serve as a technical leader and mentor to junior team members, ultimately enabling self-service capabilities and accelerating the business adoption of scalable AI/ML solutions.

Responsibilities

• Architect and Develop AI/ML Solutions: Design, implement, and deploy advanced supervised and unsupervised models (regression, classification, clustering, time-series forecasting, boosting methods) and complex neural networks (CNNs, RNNs, LSTMs).
• Lead Generative AI Initiatives: Develop and integrate solutions powered by LLMs and open-source foundation models, applying expertise in prompt engineering, fine-tuning techniques (LoRA, PEFT), and model optimization for performance, latency, and cost.
• Implement MLOps and Deployment Pipelines: Manage the full model lifecycle and deployment strategy, including model serialization (Pickle, Joblib, ONNX), containerization with Docker and Kubernetes, and building secure, scalable endpoints using FastAPI and serverless functions.
• Champion Platform Enablement: Drive adoption and utilization of the Databricks platform to accelerate use case development, promote model automation, facilitate AutoML, and create reusable template-based solutions.
• Adhere to Software Engineering Excellence: Write highly efficient, maintainable Python code (advanced Python skills required), utilizing tools like JupyterLab and VSCode, and enforce Git version control and best practices for testing and quality assurance.
• Develop User-Facing AI Applications: Build front-end tools and prototypes using Streamlit alongside standard front-end technologies (HTML/CSS/JavaScript) to demonstrate AI capabilities to business users.
• Provide Technical Leadership & Mentorship: Collaborate effectively with cross-functional teams, mentor junior engineers and data scientists, and establish governance standards for data quality, solution accessibility, and business adoption of AI/ML practices.

Qualifications

• Advanced proficiency in Python (specifically for machine learning) and extensive experience with core AI/ML open-source libraries, including scikit-learn, PyTorch, pandas, polars, NumPy, and seaborn.
• Proven experience designing and deploying end-to-end AI/ML systems, with a strong emphasis on MLOps principles and tools (Docker, Kubernetes, Git).
• Deep expertise in developing and optimizing Generative AI solutions using LLMs and foundation models, including hands-on experience with fine-tuning (e.g., LoRA) and performance optimization.
• Expertise in cloud platform deployment and infrastructure management on major cloud providers (AWS and/or Azure).
• Strong functional knowledge of Databricks for data processing, platform management, and accelerating AI/ML development.
• Experience in data processing, feature engineering, advanced visualization, and communicating complex insights effectively through storytelling.
• Demonstrated Systems Thinking approach to problem-solving, with the ability to translate high-level business goals into secure, scalable, and viable technical architectures.
• Excellent communication, collaboration, and mentorship skills, with a track record of driving best practices and team improvement.