Senior AI Engineer
Tasks
- Analyze usage patterns failure modes costs latency and performance
- Architect agentic workflows with tools memory retrieval planning and guardrails
- Build scalable ecosystems of AI systems and agents for internal teams and customers
- Contribute to AI engineering standards reusable frameworks and best practices
- Design build and deploy production grade AI systems
- Design orchestration layers between models APIs tools and retrieval systems
- Design secure explainable and production ready AI systems
- Develop solutions using LLMs RAG and AI agents
- Document architectures capabilities limitations KPIs and operational best practices
- Ensure compliance with data security governance and access control
- Ensure reliability observability scalability and maintainability
- Implement evaluation frameworks guardrails validation layers and monitoring
- Integrate AI systems with internal platforms business applications and data sources
- Prototype AI capabilities and evolve into scalable production systems
- Run pilots experiments and measure business impact
- Support adoption of AI powered workflows
Perks/Benefits
- N/A
Skills/Tech-stack
AI Agents | AWS | Anthropic | Docker | Evaluation | GCP | Gemini | Kubernetes | Langchain | Langgraph | Language Models | Large Language Models | Llamaindex | MCP | Mistral | Multimodal AI | OpenAI | Python | Retrieval-Augmented Generation | SQL | Vector Databases
Education
N/A
Roles
Regions
Countries
States
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