Gen AI Engineer
Tasks
- Automate defect triage and log summarization
- Build LLM powered tools for QA
- Create secure scalable Python services
- Deploy and operate LLM applications on AWS
- Design RAG pipelines
- Develop LLM powered agents and platforms
- Document prompts models datasets and system architecture
- Establish responsible AI guardrails and compliance
- Implement prompt engineering and evaluation frameworks
- Set up observability for LLM systems
- Write unit and integration tests with PyTest
Perks/Benefits
Skills/Tech-stack
API Gateway | AWS | AWS CDK | AWS Lambda | Agile | Amazon Bedrock | Amazon DynamoDB | Amazon ECS | Amazon EKS | Amazon S3 | Amazon SageMaker | Asyncio | Bedrock Agents | CI/CD | CloudFormation | CloudWatch | Design Patterns | Docker | DynamoDB | Embeddings | Faiss | FastAPI | Flask | GRPC | Git | IAM | Infrastructure as Code | Integration Testing | Kanban | Kubernetes | LLM Evaluation | Langchain | Llamaindex | Microservices | Model Governance | MongoDB | NoSQL | Observability | OpenSearch | PGVector | PII protection | Pinecone | PostgreSQL | Prompt engineering | Pydantic | Pytest | Python | REST APIs | Ragas | Retrieval-Augmented Generation | SOLID | SQL | Scrum | Secure Coding | Step Functions | TDD | Terraform | Test-Driven | Test-Driven Development | Tokenization | Unit Testing | Vector Databases | “as-code”
Education
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