AI/ML Solutions Architect

Job Title: AI/ML Solutions Architect Location: Washington, DC onsite Job Description The

AI/ML Solutions Architect

will be instrumental in designing and implementing end-to-end artificial intelligence and machine learning solutions 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.

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