AI Solution Architect- expert level and fully global remote

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Requirements:

• 7+ years experience, including 3+ years in AI/ML and Generative AI.
• Proven track record in designing, developing, and deploying customer-facing Generative AI solutions.
• Strong Python programming skills, with expertise in software design and optimization.
• Hands-on experience in MLOps pipelines, AWS cloud services, and DevOps practices.
• Proficiency in agentic and multi-agent AI systems, enabling autonomous reasoning, decision-making, and collaboration between AI agents.
• Experience with agentic LLM frameworks (E.g. LangChain, LlamaIndex, Crewai) for enhancing AI-driven workflows.
• Deep expertise in AWS services (SageMaker, Bedrock, Lambda, API Gateway, DynamoDB, ECS, S3).
• Proficiency in containerization technologies (Docker, Kubernetes) and CI/CD pipelines.
• Strong understanding of scalable, secure, and high-performance AI deployment strategies.

Responsibilities:

• Translate AI requirements into scalable, enterprise-grade architectures.
• Design and implement Generative AI solutions, including LLMs, RAG pipelines.
• Architect and deploy AI/ML solutions using AWS services such as SageMaker, Bedrock, Lambda, API Gateway, DynamoDB, ECS, and S3.
• Ensure AI models and solutions meet security, privacy, and compliance standards.
• Define and implement best practices for AI model deployment, orchestration, and monitoring.
• Stay updated with the latest advancements in AI and related technologies and apply them to improve existing solutions.
• Design, develop, and deploy Generative AI models for text, image, and conversational applications.
• Oversee the development and deployment of intelligent bots, ensuring they meet functional and performance requirements.
• Utilize MLOps pipelines to streamline model training, evaluation, and deployment in production.
• Implement and optimize vector databases (e.g., Pinecone, FAISS) for efficient AI retrieval and storage.
• Work with agentic LLM frameworks such as Langchain and LlamaIndex to enhance AI capabilities.
• Leverage AWS AI/ML services (EC2, S3, Lambda, SageMaker, RDS, Redshift) for scalable deployment of AI solutions.
• Utilize containerization technologies (Docker, Kubernetes) and CI/CD pipelines for machine learning model deployment.
• Design and implement AWS-based deployment strategies, ensuring scalability, security, and performance optimization.