AI Engineer
Tasks
- Automate diagnostics runbook execution and remediation
- Build LLM evaluation framework
- Build RAG pipelines with data quality validation
- Build agentic AI systems
- Create tool calling agents with retrieval and reasoning
- Curate domain knowledge and maintain knowledge freshness feedback loops
- Define reliability risk reduction and cost objective functions
- Develop retrieval pipelines and prompt synthesis
- Drive design reviews and experiment rigor
- Enforce deterministic fallbacks circuit breakers and rollback strategies
- Implement guardrails for safety compliance and least privilege access
- Implement validator models and adversarial prompt testing
- Instrument continuous evaluations for usefulness correctness and risk
- Integrate agents with observability and incident management
- Mentor peers on agent architectures and evaluation methodologies
- Optimize cost and latency using prompt engineering and caching
- Summarize post incident outcomes with traceability
- Support safe deployment patterns
- Translate production pain points into agentic roadmaps
- Use model routing distillation batching streaming and parallel tool calls
- Validate responses and implement self correction loops
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | AWS Lambda | AWS Step Functions | Amazon DynamoDB | Amazon Redshift | Amazon S3 | Amazon SageMaker | C plus plus | C# | Cloud infrastructure | CloudFormation | Cost Optimization | Docker | ECS | EKS | Fine Tuning | Function Calling | Go | Incident Management | Infrastructure as Code | Java | Kubernetes | LLM Evaluation | Language Models | Large Language Models | Latency optimization | Machine Learning | Model Deployment | Model Fine-tuning | Model Monitoring | Model Serving | Observability | Prompt engineering | Python | RAG | Retrieval-Augmented Generation | Secure tool execution | Statistics | Step Functions | Terraform | Tool Execution | Vector Databases | “as-code”
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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