AI Engineer III

Job highlights

• Generative AI / LLMs: Deep hands-on experience with multiple model providers,tool / function calling,structured generation,context engineering,and model-selection strategies
• Identify and build shared components that should be standardized across pods rather than repeatedly implemented

What Youll Do
Lead the design and implementation of complex AI systems spanning models, retrieval, tools, agents, data, APIs, and existing IQM services.
Own AI capabilities end-to-end from technical discovery and architecture through deployment, production operations, and continuous improvement.
Design scalable RAG and retrieval architectures, context and memory strategies, agent/tool workflows, and model-provider integration patterns.
Drive model and architecture decisions using systematic evaluation of quality, latency, reliability, security, scalability, and cost.
Define evaluation strategies, golden datasets, regression suites, observability, and operational SLOs for production AI systems.
Design resilience patterns including fallbacks, caching, retries, human review, failure recovery, and safe degradation.
Identify and build shared components that should be standardized across pods rather than repeatedly implemented.
Lead technical design reviews, document architecture and trade-offs, and partner with Staff Engineers/Solution Advisory Board on cross-platform implications.
Mentor AI Engineers I/II and raise engineering quality through code/design reviews and technical coaching.
Evaluate emerging models, protocols, frameworks, and platform capabilities and recommend adoption when they create measurable value.

Technical Skills & Technology Stack
Programming & Systems: Advanced Python and strong software/system design fundamentals; ability to integrate with Java or JavaScript/TypeScript services as needed.
Data: Strong SQL, Snowflake or equivalent data platforms, data pipelines, structured/unstructured data, and retrieval-oriented data design.
Generative AI / LLMs: Deep hands-on experience with multiple model providers, tool/function calling, structured generation, context engineering, and model-selection strategies.
AI Architecture: Production RAG, vector/hybrid search, ranking, agents, tool orchestration, context/memory patterns, and human-in-the-loop workflows.
Protocols & Frameworks: LangGraph/LangChain/LlamaIndex or equivalent; MCP/tool integration patterns; ability to design abstractions independent of individual frameworks/providers.
Cloud & Platform: AWS or equivalent, containers, CI/CD, service observability, distributed system fundamentals, and production operations.
Production AI: Evaluation frameworks, tracing/observability, guardrails, security, caching, fallbacks, latency/cost optimization, and failure-mode analysis.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.