Job Title: AWS Solution Architect (GenAI & Agentic AI)
Location: Fort Mill, SC or New Jersey (Hybrid) Job Summary:
We are seeking an experienced AWS Solution Architect with strong expertise in Generative AI (GenAI) and Agentic AI systems. The ideal candidate will be responsible for designing, developing, and deploying scalable AI-driven solutions on AWS, with a focus on autonomous agents, LLM-based applications, and intelligent workflows.
Key Responsibilities:
Design and architect scalable, secure, and cost-efficient solutions on AWS Cloud
Build and deploy Generative AI applications using large language models (LLMs)
Develop and implement Agentic AI systems (autonomous agents capable of decision-making and task execution)
Integrate AI/ML services such as Amazon Bedrock, SageMaker, Lambda, and API Gateway
Collaborate with business stakeholders to translate requirements into technical solutions
Lead architecture discussions, design reviews, and provide technical guidance
Ensure best practices in cloud architecture, security, and compliance
Optimize performance and cost of AI workloads on AWS
Stay updated with emerging trends in AI, GenAI, and cloud technologies
Required Skills & Qualifications:
Bachelor s or Master s degree in Computer Science, Engineering, or related field
8+ years of experience in IT with 3+ years in AWS Solution Architecture
Strong hands-on experience with AWS services (EC2, S3, Lambda, RDS, VPC, etc.)
Experience with Generative AI frameworks (OpenAI, Bedrock, Hugging Face, etc.)
Solid understanding of LLMs, prompt engineering, fine-tuning, and embeddings
Experience building Agentic AI workflows (multi-agent systems, orchestration, tool usage)
Proficiency in Python or similar programming languages
Experience with microservices architecture and REST APIs
Familiarity with DevOps practices (CI/CD, Docker, Kubernetes)
Preferred Qualifications:
AWS Certified Solutions Architect (Associate/Professional)
Experience with LangChain, AutoGen, CrewAI, or similar agent frameworks
Knowledge of vector databases (Pinecone, FAISS, etc.)
Experience with RAG (Retrieval-Augmented Generation) architectures
Exposure to MLOps and model deployment pipelines
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