Principal AI Systems Engineer

Overview

This is a senior role responsible for the hands-on design, build, validation, and deployment of Artificial Intelligence (AI) systems at Iovance Biotherapeutics. This role is focused on execution — turning approved use cases into working, validated, production AI systems that deliver measurable business value. This role directly supports Iovance’s strategy to improve overall operational productivity. The ideal candidate is a deeply technical, hands-on senior engineer with demonstrated production experience designing and shipping Large Language Model (LLM) applications, Retrieval-Augmented Generation (RAG) systems, and modern AI integrations. This position works closely with IT leads, business process owners across Manufacturing, Quality, Regulatory Affairs, Clinical, Commercial, G&A, IT Security, Privacy, and Quality Assurance to deliver AI capabilities that respect Iovance’s regulated environment. This role executes against a roadmap and prioritized backlog, while contributing technical input to refinement, scoping, and sequencing.

Primary Responsibilities

• Design, build, and ship AI systems against the approved Iovance AI roadmap, including end-to-end ownership of architecture, retrieval pipelines, prompts, evaluation, integrations, and deployment for assigned use cases.
• Implement production-grade Retrieval-Augmented Generation (RAG) systems on Iovance’s AWS infrastructure (S3, Redshift, Bedrock), including chunking strategies, embedding selection, vector storage, retrieval and reranking, grounding, and citation handling appropriate to high-accuracy use cases.
• Build, maintain, and run evaluation harnesses for AI systems, including held-out test sets, accuracy and grounding metrics, hallucination detection, adversarial inputs, and regression testing across model and prompt changes; treat evaluation as a first-class engineering deliverable, not an afterthought.
• Design and implement integrations between AI systems and Iovance enterprise systems using Model Context Protocol (MCP), APIs, and event-driven patterns, applying least-privilege access principles and partnering with IT Security on integration approval.
• Own and maintain LLM security controls for production AI systems, including input and output guardrails, prompt injection and jailbreak defenses, sensitive data redaction (PII, PHI, Iovance Confidential Information and Intellectual Property), content moderation, and abuse monitoring, working in partnership with IT Security.
• Design, develop, deploy, and maintain AI agents (multi-step reasoning systems that combine LLMs with tools, retrieval, and planning) appropriate for use in a regulated life sciences environment, including bounded scope, defined human oversight, traceability of agent decisions, and safe handling of write-back actions to systems of record.
• Establish and uphold modern engineering practices for AI development including version control for code, prompts, and evaluation sets; CI/CD pipelines; environment separation (dev, test, production); and reproducible builds.
• Conduct hands-on technical evaluation of AI vendors, tools, and Foundation Models when build-vs-buy decisions are under consideration; produce concise, fact-based recommendations to inform decisions.