Job Description

GenAI Solutions Architect-AWS

Job Title: Generative AI Solutions Architect for AWS Business Enablement Strategy

Role Summary: As the Generative AI Solutions Architect, He/She will be the catalyst for embedding artificial intelligence into the fabric of our business. Mission is to identify, design, and implement high-impact solutions using the AWS AI/ML stack, primarily focusing on Amazon Bedrock and Amazon Q. This role is not just about technology; it's about translating complex business challenges into production-ready, intelligent applications that create new value and a competitive edge.

Key Responsibilities:

Business Partnership: Collaborate directly with business leaders in Marketing, Customer Service, R&D, and Operations to identify and prioritize use cases for Generative AI.
Solution Design & Prototyping: Architect end-to-end AI solutions on AWS and rapidly build Proofs of Concept (PoCs) using Amazon Bedrock to demonstrate feasibility and business value.
Technical Leadership: Act as the company's subject matter expert on foundation models, prompt engineering, Retrieval Augmented Generation (RAG), and fine-tuning techniques within the AWS ecosystem.
Implementation & Integration: Lead the technical development and integration of AI models into new and existing business applications, using services like AWS Lambda, API Gateway, and Step Functions.
Champion Responsible AI: Establish and promote best practices for developing and deploying AI that is secure, fair, transparent, and compliant with industry regulations.

Required Qualifications:

Proven track record of technical leadership with 12+ years in the technology industry, demonstrating the ability to architect and deliver complex, large-scale solutions from concept to production.
6+ years of experience in a solutions architecture or software engineering role with a deep, hands-on focus on the AWS ecosystem.
Proven experience building solutions with Amazon Bedrock, including model evaluation, RAG patterns with Amazon Kendra or OpenSearch, and an understanding of fine-tuning.
Strong programming proficiency in Python and deep familiarity with the AWS SDK (Boto3) for interacting with AWS services.
Architectural knowledge of building secure and scalable applications using core AWS services (e.g., IAM, VPC, S3, Lambda, API Gateway).
Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.

Preferred Qualifications:

AWS Certified Solutions Architect - Professional or AWS Certified Machine Learning - Specialty.
Experience with the broader Amazon SageMaker suite for custom model training and MLOps.
Familiarity with other ML frameworks such as TensorFlow or PyTorch.
Experience deploying AI solutions in a regulated industry