AWS and Gen AI Solution Architect

Your Role

We are seeking a highly skilled Solution Architect – AWS Cloud & AI/ML to design, architect, and implement advanced AI/ML and generative AI solutions on the AWS platform. The ideal candidate will have deep expertise in large-scale distributed systems, modern AI/ML architectures, LLMs, data engineering pipelines, and AWS-native services. This role involves partnering with cross-functional teams, understanding business challenges, and crafting end‑to‑end scalable, secure, and cost‑optimized solutions

• Architect and deliver end‑to‑end AI/ML solutions on AWS, covering data ingestion, training, inference, orchestration, monitoring, and governance.
• Design and integrate LLM‑based and Generative AI solutions, including retrieval-augmented generation (RAG), prompt workflows, and production deployment strategies.
• Develop feature engineering strategies and scalable data pipelines to support ML training and real-time inference workloads.
• Lead technical discussions and provide guidance on AI/ML best practices, model lifecycle, optimization, MLOps, and model governance.

• Design highly scalable, secure, and cost-efficient architectures using:
  • Amazon SageMaker (Training Jobs, Inference Endpoints, Pipelines, Feature Store, Model Registry)
  • Amazon Bedrock (Foundation models, Generative AI orchestration, prompt management)
  • AWS Lambda, ECS, EKS, EC2 for building and orchestrating distributed AI workloads.
• Architect and optimize data engineering platforms using:
  • AWS Glue, Amazon Athena, Redshift, AWS Data Pipeline, S3, Kinesis, and related services.
• Build secure, production-grade API services for AI model inference using Amazon API Gateway and AWS compute services.

Your Profile

• 8+ years of experience in cloud architecture, with at least 5 years in AWS.
• Strong expertise in:
  • Machine Learning, MLOps, and GenAI solution design.
  • Amazon SageMaker (end‑to‑end ML lifecycle).
  • Amazon Bedrock and modern LLM architectures.
  • Data engineering with Glue, Redshift, Athena, and pipeline orchestration.
• Experience containerizing and scaling AI workloads on Lambda/ECS/EKS.
• Strong coding experience in Python and familiarity with ML frameworks (TensorFlow, PyTorch, Scikit‑learn).
• Deep understanding of security, networking, IAM, and compliance best practices for AI systems.
• Excellent communication, design thinking, and stakeholder management skills.

• AWS certifications (e.g., AWS Certified Solutions Architect – Professional, Machine Learning – Specialty).
• Experience with vector databases (e.g., Pinecone, OpenSearch, FAISS).
• Experience building RAG pipelines, multi‑agent orchestration frameworks, or custom LLM fine‑tuning workflows.
• Familiarity with DevOps/MLOps tools: GitHub Actions, Airflow, Terraform, Docker, Kubernetes