Platform Engineer III

• *Job Summary**
• --------------

Design, build, and operate enterprise\-scale AI automation solutions—spanning RAG pipelines, LLM agents, document understanding, and decisioning—with a focus on quality, reliability, performance, and measurable outcomes.

6–10\+ years total; 2–4\+ years applied AI/NLP; multiple production launches.

• *Key Responsibilities**
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• Design RAG architectures including chunking, embedding strategies, retrieval, reranking, grounding, and evaluation.
• Build tool\-enabled LLM agents with deterministic fallbacks, retry flows, and defensive error\-handling patterns.
• Implement document processing workflows (OCR, layout extraction, entity extraction, tables) using industry\-grade frameworks.
• Develop and operate inference APIs and integrate AI pipelines with event\-driven and streaming platforms.
• Build evaluation suites leveraging golden datasets, LLM\-as\-judge frameworks, and rubric scoring.
• Add deep telemetry for hallucination detection, accuracy monitoring, latency profiling, and cost management.
• Optimize prompts, retrieval strategies, and inference paths for reliability, speed, and cost efficiency.
• Implement robust security controls: input validation, prompt\-injection hardening, PII scrubbing/redaction.
• Collaborate with domain SMEs to design workflows, state machines, and business logic.

Develop reusable automation components, internal SDKs, and best\-practice templates.

• *Skill Requirements**
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• Strong Python, embeddings, vector search, and RAG/agent tooling (LangChain, Semantic Kernel, DSPy).
• Experience with at least one enterprise LLM provider (Azure OpenAI, OpenAI, Anthropic, etc.).
• Familiarity with OCR/NLP frameworks, model registries, CI/CD, Docker/K8s, and observability (OpenTelemetry).
• Knowledge of secure AI practices, cost optimization, and evaluation techniques.

• *Must Have Skill**
• ------------------

Google Cloud Platform (GCP)

• *Good to Have Skill**
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• *Other Requirements**
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