AI Architect - Gen AI, LLms & AWS

ATINFO TECHNOLOGY INC
West Palm Beach, FL, United States
27 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Job source

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Amazon Web Services Amazon S3 User Authentication Software Quality Computer Programming Databases Continuous Integration Memory Management Amazon DynamoDB Identity and Access Management
+32 more
Python (Programming Language) PostgreSQL Cloud Services Search Technologies Software Engineering Workflow Management Systems AWS Cdk Enterprise Software Applications Flask (Web Framework) Large Language Models Multi-Agent Systems Prompt Engineering Software Security Model Validation Generative AI AWS Lambda Amazon Virtual Private Cloud (VPC) Git Cloudformation Fastapi AI Platforms Kubernetes Machine Learning Operations Virtual Agents Cloudwatch Api Gateway Restful APIs Terraform GPT Serverless Computing Docker Microservices

Job description

We are looking for a hands-on AI Architect to lead the design, development, enhancement, and deployment of an enterprise AI platform powered by Large Language Models (LLMs). The ideal candidate will combine strong software engineering skills with deep expertise in Generative AI, Agentic AI, and AWS cloud services to build scalable, secure, and production-ready AI applications., * Design, develop, and enhance an enterprise AI platform integrating multiple Large Language Models (LLMs).

  • Architect and implement scalable AI solutions using modern Agentic AI frameworks and Retrieval-Augmented Generation (RAG) architectures.
  • Build intelligent AI agents capable of reasoning, planning, tool execution, and workflow orchestration.
  • Develop secure, scalable, and highly available cloud-native AI applications on AWS.
  • Design and implement REST APIs and microservices to expose AI capabilities to enterprise applications.
  • Integrate AI services with enterprise systems, databases, APIs, and third-party platforms.
  • Deploy, monitor, and optimize AI workloads on AWS ensuring performance, scalability, security, and cost efficiency.
  • Work closely with product owners and engineering teams to translate business requirements into technical solutions.
  • Drive architecture reviews, code quality, CI/CD automation, and engineering best practices.
  • Evaluate emerging LLMs, AI frameworks, and cloud services to continuously improve the AI platform.
  • Mentor developers and provide technical leadership across AI initiatives.

Requirements

  • Strong hands-on experience with OpenAI GPT, Anthropic Claude, Llama, Mistral, Amazon Nova, or similar foundation models.
  • Experience building enterprise-grade LLM applications.
  • Expertise in Retrieval-Augmented Generation (RAG).
  • Prompt engineering, prompt optimization, embeddings, semantic search, and model evaluation.
  • Experience integrating multiple LLM providers and managing model orchestration.

Agentic AI

Hands-on experience with one or more of:

  • LangChain
  • LangGraph
  • CrewAI
  • Microsoft Semantic Kernel
  • AutoGen
  • Amazon Bedrock Agents

Experience developing:

  • Multi-agent workflows
  • Tool calling
  • Function calling
  • Memory management
  • Planning and reasoning agents

AWS Cloud

Strong hands-on experience with:

  • Amazon Bedrock
  • Amazon SageMaker
  • AWS Lambda
  • ECS/EKS
  • API Gateway
  • Step Functions
  • Amazon S3
  • DynamoDB
  • Amazon OpenSearch
  • Amazon Aurora
  • CloudWatch
  • IAM
  • VPC
  • EventBridge
  • Secrets Manager

Experience with Infrastructure as Code (Terraform, AWS CDK, or CloudFormation) is highly desirable.

Programming

  • Python (mandatory)
  • FastAPI / Flask
  • REST APIs
  • Microservices
  • Docker
  • Kubernetes
  • Git
  • CI/CD pipelines

Databases & Search

Experience with:

  • PostgreSQL
  • DynamoDB
  • OpenSearch
  • Pinecone
  • Weaviate
  • Chroma
  • FAISS
  • Milvus

Preferred Qualifications

  • Experience building AI products from concept to production.
  • Strong understanding of LLMOps, MLOps, observability, and AI monitoring.
  • Experience implementing AI guardrails, responsible AI, and enterprise security controls.
  • Familiarity with event-driven and serverless architectures.
  • Knowledge of authentication, authorization, and API security.

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