Elabs Infotech
Aws Devops Architect
Designs and builds end-to-end AI/ML applications on AWS: LLM apps, RAG pipelines and agentic workflows via Amazon Bedrock, Python/FastAPI microservices, vector search, React UIs, data pipelines, and CI/CD, plus cost optimization for AI workloads.
- Compensation
- Not disclosed
- Location
- Pune; Mumbai (All Areas)
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Devops
- Posted
Skills
Must have
- 13-18 years of experience
- Design and implement LLM-based applications, RAG pipelines, agentic workflows, fine-tuning, prompt engineering
- Integrate foundation models via Amazon Bedrock (model selection, guardrails, evaluation)
- Build vector search and retrieval pipelines (Amazon OpenSearch, Kendra, Pinecone on AWS)
- Agent orchestration frameworks: LangChain, LangGraph, Bedrock Agents
- Build scalable APIs and microservices with Python (FastAPI/Flask) or Node.js
- Implement data pipelines with AWS Glue, EMR, or native Python ETL
- Manage containerized workloads via ECS/EKS
- Build responsive UIs consuming backend AI services with real-time streaming
- Integrate authentication via Amazon Cognito and role-based access control
- Architect on core AWS services: Bedrock, SageMaker, Lambda, API Gateway, S3, DynamoDB, RDS/Aurora, ECS/EKS, Step Functions
- Set up CI/CD pipelines (CodePipeline, CodeBuild, or GitHub Actions)
- Cost optimization for AI workloads: token monitoring, model routing, caching via ElastiCache
- Cloud: AWS - Bedrock, Lambda, API Gateway, S3, DynamoDB, ECS/EKS, Step Functions, IAM, CloudFormation/CDK/Terraform
- AI/ML: LLM integration, RAG, prompt engineering, LangChain/LangGraph, embeddings, vector databases
- Backend: strong Python, FastAPI/Flask, REST/GraphQL API design
- Frontend: React/Next.js or Streamlit, TypeScript/JavaScript
- DevOps: CI/CD, Docker containerization, Git
- Data: SQL/NoSQL, basic ETL, S3 data lake patterns
Nice to have
- Docker
- Java
- MySQL
- React
Full description
Full Stack AI Engineer to design build and deploy end to end AIML powered applications from frontend interfaces to cloud native backend infrastructure with a strong emphasis on AWS as the primary platform for model hosting orchestration and data pipelines
Key Responsibilities
- Design and implement LLM based applications RAG pipelines agentic workflows finetuning prompt engineering
- Integrate foundation models via Amazon Bedrock and manage model selection guardrails and evaluation
- Build and optimize vector search and retrieval pipelines Amazon OpenSearch Kendra or thirdparty vector DBs like Pinecone on AWS
- Implement agent orchestration frameworks LangChain LangGraph Bedrock Agents for multistep reasoning tasks
- Build scalable APIs and microservices using Python FastAPIFlask or Nodejs
- Implement data pipelines with AWS Glue EMR or native Python ETL for traininginference data prep
- Manage containerized workloads via ECSEKS
- Build responsive UIs that consume backend AI services
- Implement realtime streaming responses for chatstyle AI interfaces
- Integrate authentication Amazon Cognito and rolebased access control
- Architect solutions using core AWS services Bedrock SageMaker Lambda API Gateway S3 DynamoDB RDSAurora ECSEKS Step Functions
- Set up CICD pipelines CodePipeline CodeBuild or GitHub Actions AWS deploy
- Manage cost optimization for AI workloads token usage monitoring model routing caching strategies via Elasti Cache.
Required Skills
Category Skills
- Cloud AWSBedrock Lambda API Gateway S3 DynamoDB ECSEKS Step Functions IAM CloudFormationCDKTerraform
- AIMLLLM integration RAG prompt engineering LangChainLangGraph embeddings vector databases
- BackendPython strong FastAPIFlask RESTGraphQL API design
- FrontendReactNextjs or Streamlit TypeScriptJavaScript
- DevOpsCICD Docker containerization Git
- DataSQLNoSQL basic ETL S3 data lake patterns
Mandatory Skills : Amazon Bedrock, AWS Lambda, CI/CD Architecture, GenAI - LLMOps, Python
Good to Have Skills : Docker, Java, MySQL, React