GenAI Architect

We are seeking a highly experienced Gen AI Architect to design, develop, and deploy scalable Generative AI solutions in a secure and enterprise-grade AWS cloud environment. The ideal candidate will define AI architecture strategy, build AI/ML pipelines, integrate Large Language Models (LLMs), and ensure secure, scalable, and production-ready AI systems aligned with business goals.

This role requires deep expertise in Generative AI, AWS cloud architecture, machine learning, LLM integration, and enterprise system design.

Key Responsibilities

Generative AI Architecture & Strategy

• Design and implement enterprise-scale Generative AI solutions using LLMs and foundation models.

• Architect end-to-end Gen AI pipelines including data ingestion, embedding, vector storage, inference, and deployment.

• Define AI architecture patterns for scalability, security, performance, and reliability.

• Lead AI solution design reviews and provide technical leadership across teams.

AWS Cloud Architecture

• Design AI systems using AWS services such as:

  • Amazon Bedrock

  • SageMaker

  • Lambda

  • EC2

  • ECS/EKS

  • S3

  • API Gateway

  • Step Functions

  • DynamoDB / RDS

• Implement cloud-native, serverless, and containerized AI architectures.

• Ensure high availability, fault tolerance, and scalability of AI systems.

LLM Integration & Gen AI Development

• Integrate and deploy LLMs such as:

  • Amazon Bedrock Models (Claude, Titan, Llama)

  • OpenAI GPT models

  • Open-source models (Llama, Mistral, Falcon)

• Design and implement:

  • Retrieval Augmented Generation (RAG)

  • Prompt engineering frameworks

  • Fine-tuning pipelines

  • Embedding pipelines

  • Vector databases integration

Data Engineering & AI Pipelines

• Design scalable data pipelines using:

  • AWS Glue

  • Kinesis

  • Kafka

  • Airflow / Step Functions

• Integrate vector databases such as:

  • Pinecone

  • FAISS

  • OpenSearch

  • Weaviate

API & Application Integration

• Build AI-powered APIs using REST / GraphQL

• Integrate Gen AI into enterprise applications, chatbots, automation workflows, and internal tools

• Design microservices-based AI architectures

Security, Governance, and Compliance

• Implement AI security best practices including:

  • IAM roles and policies

  • Data encryption

  • Secure API access

  • AI governance and model monitoring

• Ensure compliance with enterprise security and regulatory standards

DevOps, MLOps & Deployment

• Implement CI/CD pipelines for AI models and applications

• Use tools such as:

  • Docker

  • Kubernetes

  • Terraform

  • CloudFormation

  • GitHub Actions / Jenkins

• Enable automated deployment, monitoring, and scaling

Monitoring & Optimization

• Monitor AI system performance using CloudWatch, Prometheus, Grafana

• Optimize model performance, cost, and latency

• Implement logging, alerting, and observability

Required Qualifications

• 10+ years of experience in Software Engineering / Cloud Architecture

• 3+ years of experience in Generative AI / Machine Learning Architecture

• Strong expertise in AWS cloud services and architecture design

• Hands-on experience with Amazon Bedrock, SageMaker, and AI services

• Experience integrating LLMs and building Gen AI applications

• Strong programming skills in:

  • Python

  • Java or Node.js

• Experience building scalable microservices architectures

• Experience with vector databases and embedding pipelines

• Strong understanding of REST APIs and distributed systems

Preferred Qualifications

• Experience in enterprise or regulated environments (Banking, Healthcare, Finance)

• Experience with LangChain, LlamaIndex, or similar AI frameworks

• Experience with Kubernetes and containerized deployments

• Experience with real-time AI inference systems

• AWS Certifications such as:

  • AWS Solutions Architect – Professional

  • AWS Machine Learning Specialty

Key Skills

Generative AI

• LLM Architecture

• RAG (Retrieval Augmented Generation)

• Prompt Engineering

• Fine-tuning

• Embeddings

AWS Cloud

• Bedrock

• SageMaker

• Lambda

• ECS/EKS

• S3, DynamoDB, RDS

• API Gateway

Programming & Frameworks

• Python

• LangChain

• LlamaIndex

• FastAPI

• Microservices

DevOps & Infrastructure

• Docker

• Kubernetes

• Terraform

• CI/CD pipelines

Databases

• Vector Databases

• OpenSearch

• Pinecone

• DynamoDB

• PostgreSQL

Responsibilities in Production Environment

• Architect enterprise Gen AI platforms

• Deploy scalable AI systems on AWS

• Integrate LLM-based AI solutions into enterprise applications

• Ensure security, compliance, and governance

• Optimize AI system performance and cost

• Provide technical leadership and mentoring