AI/ML Solutions Architect

DMV IT Service•Washington, DC

21d

About The Position

We are seeking an experienced AI/ML Solutions Architect to lead the design, development, and deployment of advanced machine learning, deep learning, and Generative AI solutions for a key client in the Washington, DC area. This role requires expert-level knowledge of AI/ML engineering, cloud deployment practices, MLOps automation, and Databricks platform enablement. The Architect will serve as a technical leader, influencing solution design, mentoring junior team members, and driving adoption of modern AI technologies across the organization.

Requirements

Advanced proficiency in Python with expertise in machine learning development.
Hands-on experience with major AI/ML libraries: scikit-learn, PyTorch, pandas, polars, NumPy, seaborn.
Proven experience designing and deploying full end-to-end AI/ML solutions in production environments.
Strong MLOps skills with Docker, Kubernetes, Git, CI/CD, and model deployment workflows.
Deep experience developing Generative AI solutions and fine-tuning LLMs (e.g., LoRA, PEFT).
Strong cloud experience (AWS or Azure), including deploying ML workloads in production.
Functional expertise with Databricks for ML pipelines, model management, and automation.
Strong ability to translate business goals into scalable, secure, technical architectures.
Excellent communication, collaboration, and leadership abilities.

Nice To Haves

Experience integrating AI/ML into enterprise systems or regulated environments.
Hands-on experience with MLflow, Feature Store, or similar operational tools.
Background in building RAG systems or vector database integrations.
Prior experience mentoring or leading engineering/data science teams.
Experience with data storytelling and advanced visualization techniques.

Responsibilities

Design and implement advanced supervised and unsupervised ML models including regression, classification, clustering, boosting, and time-series forecasting.
Architect deep learning solutions including CNNs, RNNs, LSTMs, and transformer-based architectures.
Translate business requirements into scalable, secure, and production-ready ML solutions.
Lead development and integration of Generative AI solutions using large language models and open-source foundation models.
Apply prompt engineering, fine-tuning techniques including LoRA and PEFT, and model optimization for performance, latency, and cost efficiency.
Build RAG (Retrieval-Augmented Generation) systems and other GenAI patterns as needed.
Implement full model lifecycle management including model packaging (Pickle, Joblib, ONNX) and CI/CD workflows.
Deploy secure and scalable ML endpoints using Docker, Kubernetes, FastAPI, and serverless compute.
Build automated monitoring, versioning, testing, and retraining pipelines aligned with modern MLOps frameworks.
Drive platform utilization for data processing, AutoML, MLflow model tracking, and scalable compute.
Build reusable templates, accelerators, and solution frameworks to speed adoption.
Train and enable teams to use Databricks effectively for AI/ML workloads.
Develop maintainable, well-structured, and high-performance Python code.
Utilize JupyterLab, VSCode, Git, and automated testing frameworks.
Ensure engineering best practices in code reviews, design decisions, and documentation.
Build prototype tools, dashboards, and interactive AI applications using Streamlit.
Integrate front-end technologies (HTML, CSS, JavaScript) where needed to support business-facing interfaces.
Coach and mentor junior engineers and data scientists.
Work closely with cross-functional teams including data engineering, product, DevOps, and business stakeholders.
Establish governance best practices for data quality, security, and responsible AI usage.