AI Engineer II - Onsite, Springfield, MO
USD 77K-176K (estimate) Mid-level Full Time
Tasks
- Apply MLOps and LLMOps best practices
- Assess data availability and quality for AI readiness
- Build and deploy LLM based RAG and generative AI solutions
- Build and operate agent workflows with human in the loop approvals
- Collaborate with stakeholders to define AI success metrics
- Define and meet SLOs SLAs and cost budgets
- Design and validate AI ready data pipelines
- Design end-to-end AI workflows
- Enable observability with tracing and token cost monitoring
- Implement PromptOps and prompt versioning
- Implement model lifecycle management and rollback
- Implement responsible AI governance with RBAC IAM and audit logging
- Implement unit integration and contract testing for AI pipelines
- Integrate AI solutions via APIs and event driven architectures
- Participate in on-call rotation
- Perform load and cost testing for inference
- Prepare and transform data using SQL or Python
- Research and evaluate emerging AI technologies
- Run A B evaluations for accuracy safety and groundedness
- Translate business needs into technical requirements
Perks/Benefits
Skills/Tech-stack
AWS | Audit Logging | Autoscaling | Azure | Bias detection | CI/CD | Cost monitoring | Data Contracts | Data Lineage | Docker | Drift Detection | Event Driven | Event-driven architecture | Experiment tracking | Experimentation | Explainability | Git | Google Cloud | IAM | Kubernetes | LLMOps | Language Models | Large Language Models | Load Testing | MLOps | Model Registry | Observability | PII Redaction | Prompt engineering | PromptOps | Python | RAG | RBAC | REST APIs | Retrieval-Augmented Generation | SQL | Schema Validation | Schema drift | Schema drift detection | Secrets management | Semantic Search | Semantic Versioning | Snowflake | Telemetry | Token Cost Monitoring | Tracing | Vector Database
Education
Bachelor of Arts | Bachelor of Engineering | Bachelor of Science
Roles
AI | AI Engineer | Engineer
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