AIML Solutions Architect

Washington, AR - USA

Monthly Salary: Not Disclosed

Posted on: 1 hour ago

Vacancies: 1 Vacancy

Job Summary

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.

Required Skills :

Basic Qualification :

Additional Skills :

Background Check : No

Drug Screen : No

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...

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.