AI Engineer III

Base Salary Range USD $133,836.99 - USD $150,000.00 /Yr. Join Mariner Finance! Since 1927, the Mariner Finance family of companies has provided customers with creative, flexible, and convenient lending options. Headquartered in Baltimore, Mariner Finance operates coast-to-coast with physical locations in over half the states. With a growing number of employees, superior customer service remains the cornerstone of our business, and we pride ourselves in delivering a variety of loans with an enhanced focus on exceptional service. We work with customers to find options that are beneficial to their specific needs, which is why we are recognized by our customers as one of the community’s consumer finance companies of choice. Benefits: For information regarding our benefits, please visit: https://www.marinerfinance.com/careers/benefits/  All full time employees are provided with a generous benefits package in addition to their monetary compensation. Learn more about it today. Job Details In this role, you will... Be responsible for building and maintaining the knowledge layer and backend services that power enterprise AI agents. Develop the skills and tool registries, knowledge graph components, vector search indexes, data integration pipelines, and agent evaluation suites that help turn standalone models into reliable, governed, tool-using agents. Partner with Level 4 engineers, the AVP of AI Engineering, Data, Security, and business stakeholders to deliver well-scoped components of the platform. Apply established engineering patterns, contribute to design discussions, and grow technical depth in agentic AI and knowledge engineering. Responsibilities and Duties: Build and maintain components of the enterprise knowledge layer that agents use for grounding, memory, and context, including skills, knowledge graph elements, and vector indexes, working from designs set by senior engineers and contributing improvements. Develop and maintain parts of the skills and tool registry, including skill and tool definitions, versioning, discovery, and publishing workflows, and build MCP servers and APIs that expose capabilities to agent builders. Implement knowledge graph pipelines, including entity extraction, entity resolution, relationship loading, and schema updates, and build graph and hybrid retrieval queries for agent use cases. Build and tune vector search, including embedding generation, semantic chunking, metadata filtering, hybrid search, and index refresh jobs, and measure retrieval quality against agreed metrics. Build batch and streaming data pipelines on Databricks (Delta, Spark, Structured Streaming) and modern ETL/ELT patterns to ingest structured, semi-structured, and unstructured data into the knowledge layer, with data quality checks and lineage. Build integrations between business systems, document stores, and the knowledge layer using secure REST APIs and connectors. Register and govern data and AI assets through Unity Catalog. Implement access controls and entitlement checks in retrieval paths so agents return only data that a given user and agent are permitted to see. Build agent evaluation suites, including golden datasets, synthetic test cases, LLM-as-judge and rule-based scorers, and regression tests that measure retrieval quality, groundedness, tool-selection reliability, task completion, safety, latency, and cost. Integrate evaluations into CI/CD pipelines as quality gates and review evaluation results to identify regressions and improvement opportunities. Write clean, asynchronous, production-ready Python and robust web APIs (such as Flask or FastAPI) that power agent actions and expose knowledge services. Build and integrate agentic tools and workflows for Databricks Custom Agents. Apply context and prompt engineering techniques to manage context windows and produce structured outputs (such as JSON schemas) from tool-calling agents. Implement monitoring, tracing, and logging for agent loops, tool usage, and retrieval systems, and follow security practices for identity, secrets, runtime isolation, and data store protection. Contribute to CI/CD pipelines, test triggers, and multi-environment deployments, deploy services on AWS (including EC2), and use infrastructure as code where available. Participate in code reviews and follow Git branching and merge practices. Create and maintain technical documentation, runbooks, and lineage records that support auditability, supportability, and responsible AI governance. Troubleshoot and resolve issues in pipelines, retrieval quality, integrations, and agent behavior, escalating complex issues to senior engineers as needed. Partner with engineers, data owners, and business stakeholders to understand requirements and deliver reliable components. Learn from code reviews and technical guidance, and share knowledge with peers. Perform additional duties as assigned to support evolving business needs. Required Qualifications: Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, Information Systems, or a related field; additional, applicable years of experience may be substituted for a bachelor’s degree. Minimum of six (6) years of experience in software engineering, data engineering, platform engineering, machine learning engineering, or a related technology discipline, with hands-on experience in AI, machine learning, or LLM-based solutions (production work, substantial projects, or formal training). Strong Python programming skills, including asynchronous programming and testing, and experience building REST APIs (such as Flask or FastAPI). Experience building data pipelines using ETL/ELT patterns and SQL, preferably on Databricks or a comparable platform such as Snowflake. Working experience with vector databases or vector search, embeddings, chunking strategies, and retrieval-augmented generation (RAG) pipelines. Working knowledge of knowledge graphs