IQVIA
life sciences and healthcare
AISupportEngineer
Neural analysis suggests this role is
optimal for Mid candidates.
“AI Support Engineer at IQVIA. Skills: Agentic AI, Production Support, Root Cause Analysis. Operate Agentic AI products. Support Agentic AI products”
What You'll Achieve.
Ensure AI agents operate reliably; Ensure AI agents operate safely; Ensure AI agents meet expectations; Enable rapid issue resolution; Provide operational insights; Enable continuous learning; Ensure stable operation of AI agents; Ensure predictable operation of AI agents; Ensure auditable operation of AI agents; Improve prompt engineering; Refine agent policy; Refine agent guardrails; Enhance workflow orchestration; Ensure agent outputs are understandable; Ensure agent outputs are explainable; Provide clear guidance on thresholds; Provide clear guidance on guardrails; Help users make effective use of products; Reflect new agent capabilities; Reflect model upgrades; Reflect workflow changes; Reflect feature releases; Reduce operational toil; Improve agent reliability; Increase user trust; Increase user adoption
Industry & Context.
Analytical mindset; Reason about AI system behavior
What They're Looking For.
Must Have
Python, SQL, Agentic AI concepts, Logs, telemetry, and observability in AI systems, Agentic libraries such as LangChain, LangGraph, LangSmith, Machine Learning fundamentals
Nice to Have
big-data or distributed technologies, Hive, Kafka, HDFS, Impala
What You'll Do.
Operate Agentic AI products
Support Agentic AI products
Improve Agentic AI products
Provide technical support
Investigate root causes
Feed operational insights
Act as human-in-the-loop
Develop knowledge base
Maintain knowledge base
Track operational KPIs
Publish operational KPIs
Identify opportunities to reduce toil
Improve agent reliability
Increase user adoption
How You'll Work.
Team & Collaboration
Act as key interface between users, AI product teams, and engineering; Work closely with AI engineers and architects; Collaborate with Product Managers; Collaborate with Product Owners; Collaborate with Engineering teams; Collaborate with Data platform teams; Collaborate with upstream/downstream application teams; Participate in product syncs; Participate in engineering syncs
Communication Scope
Explain complex agent behaviors to non-technical users
Full Job Description
**Overall, Purpose of the Job** We are hiring for the AI & Technology Solutions (ATS), Clinical AI & Technology Innovation (CAITI) – Technical Support Engineering (TSE) team. This team is responsible for operating, supporting, and continuously improving Agentic AI products running in regulated production environments. The role focuses on first‑line and second‑line technical support for multi‑agent AI workflows, ensuring that AI agents, orchestration layers, data pipelines, and downstream integrations operate reliably, safely, and in line with enterprise and regulatory expectations. The incumbent will act as a key interface between end users, AI product teams, and engineering, enabling rapid issue resolution, operational insights, and continuous learning across Agentic AI systems developed by the ADSC team. **Principal Accountabilities** 1\. Agentic AI Production Support & Operations · Serve as the single point of contact (L1/L2) for users of Agentic AI products across multiple ADSC offerings, supporting multi‑agent workflows in live production environments. · Monitor, triage, and resolve incidents related to: o Agent orchestration and failures o Tool/ skill execution errors o Data access and pipeline issues o Model degradation or unexpected agent behavior · Ensure stable, predictable, and auditable operation of AI agents in regulated clinical and analytics contexts. 2\. Agent Behavior Analysis & Root Cause Investigation · Analyze agent execution logs, prompts, tool calls, and system telemetry to: o Identify failure patterns o Perform root cause analysis (RCA) across agent, application, and infrastructure layers o Distinguish between model issues, orchestration logic issues, data issues, and user‑input driven failures · Work closely with AI engineers and architects to feed operational insights back into: o Prompt improvements o Agent policy and guardrail refinement o Workflow and orchestration enhancements 3\. Human‑in‑the‑Loop & User Enablement · Act as a human‑in‑t
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