AI/ML Solution Architect

Role: AI/ML Solution Architect

Contract:12 Months

Location: Hybrid in Reston, VA - 1 day onsite

• Resources must be within commutable distance to Northern Virginia. There is a requirement to be on-site on Wednesdays.

Overview:

Our client is seeking an experienced Solution Architect to support various GenAI and Machine Learning projects. This role is pivotal in designing and implementing advanced AI solutions within our AWS-powered infrastructure. The AI/ML Solution Architect will be responsible for creating robust overall end-to-end solutions for GenAI projects that integrate seamlessly with our existing IT framework and support our ambitious goals for technological innovation and efficiency. This position is ideal for someone passionate about harnessing the power of AI technologies and building end-to-end solutions to drive significant business impact. This role is pivotal in designing and implementing advanced AI solutions within our AWS-powered infrastructure. The AI/ML Solution Architect will be responsible for creating robust overall end-to-end solutions for GenAI projects that integrate seamlessly with our existing IT framework and support our ambitious goals for technological innovation and efficiency. This position is ideal for someone passionate about harnessing the power of AI technologies and building end-to-end solutions to drive significant business impact. This role is pivotal in designing and implementing advanced AI solutions within our AWS-powered infrastructure. The AI/ML Solution Architect will be responsible for creating robust overall end-to-end solutions for GenAI projects that integrate seamlessly with our existing IT framework and support our ambitious goals for technological innovation and efficiency. This position is ideal for someone passionate about harnessing the power of AI technologies and building end-to-end solutions to drive significant business impact.

Responsibilities:

Architect and design AI/ML solutions that leverage AWS services and a variety of GenAI and ML models to meet critical business needs.

Collaborate with Data Scientists and Machine Learning Engineers to create a robust conceptual, logical, and practical solution architecture that supports the deployment and scaling of AI models.

Develop technical roadmaps that align with product roadmaps.

Ensure that AI solutions comply with data privacy and security protocols while integrating smoothly with legacy systems.

Build solution architecture that optimizes the performance of AI systems by leveraging AWS's scalable environment and manage resource allocation to maximize efficiency.

Provide architecture guidance in AWS AI services such as SageMaker, Lambda, Bedrock, Llama, and other AWS Machine Learning models to streamline development and deployment processes.

Support training and development efforts to enhance the team's understanding and use of AWS cloud solutions in AI projects.

Evaluate new technologies and AWS updates to continuously improve and expand AI capabilities within the company.

Co-develop and drive the AI/ML strategy, ensuring alignment with enterprise capability maturity and progression.

Provide thought leadership in enterprise AI/ML architecture, ensuring scalability, resilience, and alignment with organizational goals.

Adhere to and implement architecture governance frameworks to ensure best practices and consistency across AI/ML initiatives.

Apply principles of Responsible AI to ensure ethical and transparent AI development and deployment.

Evaluate and recommend market-leading tools and technologies, such as TensorFlow, PyTorch, Hugging Face, Databricks, and in-house platforms, to enhance AI/ML capabilities.

Foster collaboration across teams, ensuring influence and alignment in cross-functional AI/ML initiatives.

AI Solution Architecture: Lead the architecture and design of GenAI and Machine Learning solutions to address key business challenges and opportunities within the healthcare domain.

AWS AI/ML Service Expertise: Leverage in-depth knowledge and hands-on experience with AWS AI/ML services, including but not limited to SageMaker, Lambda, and Bedrock, to design and implement AI-based solutions.

Foundational AWS Architecture: Architect solutions utilizing foundational AWS services such as EC2, S3, EKS, and others, ensuring scalability, reliability, and security.

End-to-End Solution Design: Design complex, end-to-end AI-powered solutions, considering data ingestion, processing, model development, deployment, integration with existing systems, and ongoing monitoring.

AI Model Evaluation and Integration: Participate in the evaluation of new and emerging GenAI and ML models, and architect solutions for their effective integration into healthcare applications and workflows.

Collaboration and Communication: Collaborate closely with data scientists, engineers, product managers, and business stakeholders to understand requirements and translate them into robust architectural designs.

Security and Compliance: Design solutions with a strong focus on security, privacy, and compliance requirements within the healthcare industry (e.g., HIPAA).

Technical Guidance: Provide technical guidance and mentorship to development teams on the implementation of AI/ML solutions on AWS.

Documentation: Create and maintain comprehensive architectural documentation, including diagrams, specifications, and design patterns.

Technology Evaluation: Stay abreast of the latest advancements in AI/ML and AWS services, and evaluate their potential application within the organization.

Required Skills & Experience:

Bachelor s or Master s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.

Extensive experience with AWS cloud services, particularly those related to AI and ML like AWS SageMaker, Lambda, and EC2.

Proven track record in designing and deploying AI/ML architectures and solutions in a large-scale environment.

Strong understanding of machine learning algorithms and AI implementation challenges.

Experience with the software development life cycle (SDLC) and agile methodologies.

Demonstrated expertise in strategic planning and enterprise AI/ML capability development.

Strong financial acumen to manage AI/ML investments and ensure cost-effective implementation.

Proven experience as a Solution Architect with a significant focus on supporting Generative AI and Machine Learning projects.

In-depth knowledge and substantial hands-on experience with AWS AI/ML services (SageMaker, Lambda, Bedrock, etc.).

Minimum of 3-5 years of experience architecting and building solutions leveraging core AWS services (EC2, S3, EKS, IAM, networking, etc.).

Relevant AWS Certification(s) are required, with preference for certifications related to AI/ML (e.g., AWS Certified Machine Learning Specialty).

Demonstrated experience in designing AI-based solutions, such as chatbots, LLM-powered capabilities, recommendation systems, or predictive analytics in a production environment.

Architecturally focused with the ability to design complex, scalable, and future-proof end-to-end solutions, as opposed to a pure development or data science background.

General understanding of underlying AI/ML tools and languages such as Python, TensorFlow, and PyTorch (architectural understanding of their role in the ecosystem, not requiring deep developer-level expertise).

Excellent verbal and written communication skills, with the ability to effectively communicate complex technical concepts to both technical and non-technical audiences. Strong collaboration and interpersonal skills are essential.

Preferred Skills & Experience:

AWS Certified Machine Learning Specialty or AWS Certified Solutions Architect certification.

Strong leadership skills with the ability to manage cross-functional teams.

Excellent problem-solving, organizational, and analytical abilities.

Effective communication skills to articulate complex technical ideas to non-technical stakeholders.

Experience with enterprise architecture tools such as TOGAF, ArchiMate, or similar frameworks.

Proficiency in Kubernetes, Docker, and data pipeline tools such as Apache Kafka and Airflow.

Familiarity with the healthcare industry, its data landscape, regulations, and common use cases for AI/ML.