AWS and Gen AI Solution Architect
Location- PAN India
Experience-10+ Years
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.
Key Responsibilities
AI/ML & Generative AI Architecture
- 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.
AWS Cloud Architecture
- 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.
Solution Development & Technical Leadership
- Work with product, engineering, and business teams to convert requirements into scalable AWS-based AI solutions.
- Lead design reviews, create architecture diagrams, and document cloud patterns and reusable frameworks.
- Identify and manage risks related to scalability, performance, data quality, model drift, and security.
- Mentor engineering teams on AWS, AI/ML frameworks, and cloud-native development.
Required Skills & Experience
- 10+ 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.
Preferred Qualifications
- 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
