AI/ML Solutions Architect

Location: Washington, DC
Employment Type: Full-time

DMV IT Service LLC, founded in 2020, is a trusted IT consulting firm specializing in advanced AI/ML solutions, cloud engineering, data analytics, cybersecurity, and enterprise technology modernization. We partner with commercial and government clients to deliver high-impact digital transformation initiatives. Our team is committed to excellence through innovation, expert consulting, and end-to-end technology enablement.

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.

• 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.

• 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.

• 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.