AI/ML Engineer

Job Description

Working with a Randstad client in the heart of Washington, D.C., you will serve as a pivotal bridge between cutting-edge research and production-grade software. This role is designed for a systems-thinking leader who can navigate the entire lifecycle of artificial intelligence—from architecting sophisticated neural networks and fine-tuning Large Language Models (LLMs) to engineering the MLOps pipelines that keep them running. You will not only build the models but also own the platform enablement within Databricks, ensuring that AI solutions are scalable, secure, and deeply integrated into business operations. As a senior member of the technical team, you will champion software engineering best practices, mentor junior talent, and translate complex business challenges into high-impact, user-facing AI applications.

Key Responsibilities

End-to-End Model Development: Design, implement, and optimize a diverse range of models including regression, time-series forecasting, and deep learning architectures (CNNs, RNNs, LSTMs).

Generative AI Innovation: Lead the integration of LLMs and foundation models using advanced techniques like LoRA and PEFT, while balancing performance, cost, and safety.

Platform & MLOps Orchestration: Drive Databricks adoption and automate model lifecycles using Docker, FastAPI, and serverless functions to build secure, scalable endpoints.

Full-Stack AI Tooling: Create intuitive, user-facing interfaces for AI tools using Streamlit and standard front-end technologies (HTML/CSS/JavaScript).

Systems Architecture: Apply a holistic mindset to technical problems, ensuring data quality, infrastructure stability, and seamless downstream application integration.

Leadership & Mentorship: Act as a technical subject matter expert to mentor junior engineers and collaborate with cross-functional teams to improve AI governance and business adoption.

Technical Qualifications

Advanced Python Programming: Mastery of Python and its core data ecosystem, including pandas, polars, NumPy, scikit-learn, and PyTorch.

GenAI Expertise: Proven experience in prompt engineering, fine-tuning foundation models, and evaluating LLM latency and performance.

Cloud & Modern DevStack: Hands-on experience with AWS or Azure, version control (Git), and containerization (Docker).

Data Engineering & Visualization: Proficiency in advanced data cleaning, feature engineering, and storytelling through visualization libraries like Seaborn.

Software Best Practices: Strong background in "Modern Code" development using VSCode, JupyterLab, and CI/CD principles.

Platform Knowledge: Deep technical expertise in Databricks, including its AutoML and model automation capabilities.