Sr. Analyst, Applied AI Engineer

Lincoln Financial•Radnor, PA

2d•$96,900 - $176,200•Hybrid

About The Position

The Sr. Applied AI Engineer designs and builds intelligent services and agentic workflows that sit on top of the cloud, data, and application stack. This role creates production-ready solutions for ranking, summarization, retrieval, classification, recommendation, and decision support using Python and governed enterprise AI development practices.

Requirements

4 Year/Bachelor's degree or equivalent work experience (4 years of experience in lieu of Bachelor's)
5 – 7+ Years of experience in ML engineering, applied AI, NLP, LLM engineering, that directly aligns with the specific responsibilities for this position.
5+ years of experience in ML engineering, applied AI, NLP, LLM engineering, or a related field.
Strong Python experience.
Experience productionizing AI or ML workflows on AWS or similar cloud platforms.
Familiarity with Databricks, MLflow, model evaluation, and governed data environments.
Experience working with SQL and distributed processing tools such as PySpark.
Strong communication and product collaboration skills.

Nice To Haves

Experience with SageMaker Studio, AgentCore are plus

Responsibilities

Leads the design and implementation of AI solutions for ranking, summarization, extraction, classification, recommendation, and conversational workflows across complex or multi-stakeholder use cases.
Develops prompt-based and model-based solutions using Python and modern AI orchestration patterns, independently evaluating technical options and recommending scalable approaches.
Works across structured and unstructured data in cloud and enterprise data environments to design reliable, governed AI workflows that integrate with broader data and application ecosystems.
Establishes and enhances evaluation, testing, and monitoring approaches for AI-enabled workflows and agentic systems to improve accuracy, quality, reliability, and operational readiness.
Partners with business, engineering, product, and governance stakeholders to translate ambiguous operational pain points into production AI features, success measures, and implementation plans.
Advances reusable prompt libraries, agent patterns, and service interfaces for internal AI products, promoting consistency, scalability, and adoption across teams.
Applies and helps strengthen responsible AI practices, including grounding, escalation paths, review controls, policy constraints, and measurable success criteria for AI-enabled solutions.
Provides technical guidance to peers and project teams by sharing best practices, reviewing solution approaches, and influencing adoption of effective AI engineering patterns.