{"id":3200400,"url":"https://alion.io/job/equifax-agentic-ai-optimization-developer","title":"Agentic AI Optimization Developer","company":{"id":4816,"name":"Equifax","domain":"equifax.com","url":"https://alion.io/company/equifax","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":75,"open_postings":5,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":50,"computed_at":"2026-10-25T05:49:26Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Toronto, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":70000,"max_usd":152000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":25},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Hallucination","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Maestro","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Synthetic Data","optional":false},{"name":"TypeScript","optional":false},{"name":"UiPath","optional":false},{"name":"LangChain","optional":true}],"status":"live","first_seen_at":"2026-10-23T00:00:00Z","employer_posted_date":"2026-10-23","last_verified_at":"2026-10-24T23:06:49Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Synopsis of the role\nAt Equifax, we are moving past passive AI chat interfaces to build the future of autonomous workflows. We are creating intelligent, self-correcting multi-agent systems that can navigate complex software environments, utilize external tools, and solve open-ended business problems with minimal human intervention. To ensure these systems are safe, reliable, and enterprise-grade, we are seeking an analytical Agentic AI Evaluation & Tuning Engineer.\nIn this role, you will be the guardian of our production AI reliability. You will bridge the gap between raw Large Language Model (LLM) capabilities and flawless autonomous execution. Unlike traditional software testers or prompt engineers, you will focus on the behavior, decision-making logic, tool-use efficiency, and long-term stability of multi-agent architectures. Your mission is to build the automated evaluation frameworks that keep our agents accurate, cost-effective, and hallucination-free.\nWhat you will do\nGolden Dataset Curation & Automated Evaluation\nBuild the \"Golden Set\":Curate, maintain, and augment high-quality reference datasets (Golden Sets) of documents, user queries, and expected agent trajectories to serve as the ultimate source of truth for testing.\n\nAutomate Eval Cycles:Design and implement automated, continuous evaluation pipelines to measure agent accuracy, latency, token spend, and fallback reliability before code hits production.\n\nTrajectory & Reasoning Auditing:Trace and dissect complex, multi-step agent \"thought\" processes (e.g., ReAct, Reflection loops) to pinpoint exactly where an agent deviates from its intended logic path.\n\nAgent Tuning & Developer Collaboration\nBehavioral Optimization:Refine system prompts, context windows, and few-shot examples to optimize how agents execute complex, multi-step workflows.\n\nTool & Function-Calling Optimization:Fine-tune how agents interact with external APIs, databases, and UiPath RPA workflows-minimizing execution errors, redundant calls, and token overhead.\n\nAugment Development:Partner closely with AI Solution Leads and AI Agent Developers to feed evaluation insights back into the development lifecycle, helping them build robust, reusable, and self-correcting agent components.\n\nProduction Guardrails & Lifecycle Management (LLMOps)\nDefeat Drift & Hallucinations:Actively monitor deployed agents to identify, troubleshoot, and mitigate semantic drift, prompt injections, infinite execution loops, and hallucinations.\n\nMaintain Autonomous Integrity:Implement robust guardrail frameworks to ensure agents maintain reliable, fact-based autonomous decision-making post-deployment in production.\n\nRAG & Knowledge Integration:Optimize Domain-Specific Knowledge Bases and Retrieval-Augmented Generation (RAG) pipelines to ensure agents pull from accurate data rather than assumptions.\n\nWhat Experience You Need\nExperience:3+ years of professional experience in software quality engineering, test automation, or data/ML engineering, with a dedicated focus on LLM testing, prompt tuning, or orchestration patterns over the last 1-2 years.\n\nAgentic & LLM Frameworks:Proven hands-on experience working with LLM orchestration frameworks (e.g., LangGraph, ADKs or specialized internal SDKs).\n\nFunction Calling Mastery:Deep understanding of JSON schema design for LLM tool-calling, function-calling, and structured outputs.\n\nAdvanced Debugging & Automation:Strong background in writing automated test scripts (Python-heavy) and using tracing/observability concepts to debug cascading errors in asynchronous, non-deterministic systems.\n\nWhat Could Set You Apart\nExperience with AI evaluation and observability platforms\n\nLive production experience testing Agentic workflows and GenAI solutions\n\nFamiliarity with Google Cloud AI suite (Vertex & Gemini Enterprise Agent Platform) and UiPath ecosystem (Maestro).\n\nExperience utilizing LLMs to securely generate high-quality synthetic data for edge-case testing.\n\nProficiency in Python or TypeScript, with a deep understanding of asynchronous programming, API design, and microservices architecture.\n\nDemonstrated learning agility and a proactive approach to mastering new technologies.\n\nThis is a newly created position.\nPrimary Location:\nCAN-Toronto-5700 YongeFunction:\nFunction - 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