Sr. Applied AI Engineer

Responsibilities

• Design, build, deploy, and support production-grade agentic AI systems that operate against explicit goals, constraints, policies, and guardrails.
• Build agent orchestration patterns for multi-step workflows, tool calling, MCP servers, state management, memory, retries, recovery paths, and human-in-the-loop controls.
• Partner closely with Product, UX, Design, Architecture, Security, and Engineering teams to create AI experiences that are useful, understandable, reliable, and aligned with real user workflows.
• Develop and operate RAG systems that ground model behavior in enterprise knowledge, including ingestion, chunking, embeddings, vector and hybrid retrieval, reranking, retrieval evaluation, and citation or traceability strategies.
• Define and implement evaluation frameworks for AI systems, including offline test sets, regression suites, adversarial testing, groundedness and faithfulness scoring, task completion metrics, and production quality monitoring.
• Instrument agentic systems for observability, including traces of model calls, prompts, tool usage, decisions, retrieved context, latency, cost, errors, and user feedback.
• Establish safeguards for responsible AI use, including prompt injection defense, data access controls, PII protection, bias and toxicity detection, misuse prevention, audit logging, and policy enforcement.
• Optimize model selection, prompts, context windows, caching, routing, inference patterns, latency, throughput, reliability, and cost across production workloads.
• Mentor engineers on applied AI practices, including prompt and context engineering, agent design, RAG, evaluation, safety, observability, and production support.
• Stay current with emerging AI platforms, frameworks, models, and standards.

Qualifications

• 5+ years of software engineering experience with strong Python proficiency.
• 2+ years building production ML or agentic AI systems.
• 1+ years hands-on experience with agentic frameworks (LangGraph, CrewAI, AutoGen, or equivalent).
• Built production AI systems including agents, MCP servers, multi-step reasoning, and multi-turn conversation.
• Deployed RAG systems including embedding models, vector databases, hybrid search, and retrieval optimization.
• Designed LLM strategies covering tool calling, structured outputs, prompt engineering, and context window management.
• Implemented AI safety and evaluation pipelines covering bias detection, PII leakage, faithfulness scoring, toxicity, and prompt injection mitigation.
• Optimized models for inference efficiency, latency, and cost management.