{"id":3619630,"url":"https://alion.io/job/mariner-finance-ai-engineer-iii","title":"AI Engineer III","company":{"id":2256507,"name":"Mariner Finance","domain":"marinerfinance.com","url":"https://alion.io/company/marinerfinance","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"iCIMS","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Plano, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":133836,"max":150000,"currency":"USD","period":"year","gross":null,"usd_annual":150000},"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon EC2","optional":false},{"name":"Amazon Neptune","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Embeddings","optional":false},{"name":"ETL/ELT","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Neo4j","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Docker","optional":true},{"name":"GraphRAG","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"MLFlow","optional":true},{"name":"Terraform","optional":true}],"status":"closed","first_seen_at":"2026-11-02T17:52:40Z","employer_posted_date":"2026-11-02","last_verified_at":"2026-11-04T03:55:31Z","board_verified":false,"closed_at":"2026-11-04T03:55:31Z","days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"Base Salary Range\nUSD $133,836.99 - USD $150,000.00 /Yr.Join Mariner Finance!\nSince 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.\nBenefits:\nFor information regarding our benefits, please visit: https://www.marinerfinance.com/careers/benefits/\nAll full time employees are provided with a generous benefits package in addition to their monetary compensation. Learn more about it today.\nJob Details\nIn this role, you will...\nBe 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.\nPartner 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.\nResponsibilities and Duties:\nBuild 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.\nDevelop 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.\nImplement knowledge graph pipelines, including entity extraction, entity resolution, relationship loading, and schema updates, and build graph and hybrid retrieval queries for agent use cases.\nBuild and tune vector search, including embedding generation, semantic chunking, metadata filtering, hybrid search, and index refresh jobs, and measure retrieval quality against agreed metrics.\nBuild 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.\nBuild 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.\nImplement access controls and entitlement checks in retrieval paths so agents return only data that a given user and agent are permitted to see.\nBuild 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.\nIntegrate evaluations into CI/CD pipelines as quality gates and review evaluation results to identify regressions and improvement opportunities.\nWrite 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.\nApply context and prompt engineering techniques to manage context windows and produce structured outputs (such as JSON schemas) from tool-calling agents.\nImplement 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.\nContribute 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.\nCreate and maintain technical documentation, runbooks, and lineage records that support auditability, supportability, and responsible AI governance.\nTroubleshoot and resolve issues in pipelines, retrieval quality, integrations, and agent behavior, escalating complex issues to senior engineers as needed.\nPartner 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.\nPerform additional duties as assigned to support evolving business needs.\nRequired Qualifications:\nBachelor’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.\nMinimum 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).\nStrong Python programming skills, including asynchronous programming and testing, and experience building REST APIs (such as Flask or FastAPI).\nExperience building data pipelines using ETL/ELT patterns and SQL, preferably on Databricks or a comparable platform such as Snowflake.\nWorking experience with vector databases or vector search, embeddings, chunking strategies, and retrieval-augmented generation (RAG) pipelines.\nWorking knowledge of knowledge graphs or graph databases (such as Neo4j, Amazon Neptune, or RDF/SPARQL) and graph or ontology modeling concepts.\nExperience or demonstrated familiarity with building evaluation datasets and automated scoring for LLM or agent outputs.\nFamiliarity with agentic tooling, including tool calling, structured outputs, and agent frameworks, and awareness of Model Context Protocol (MCP) or similar tool-integration approaches.\nFamiliarity with monitoring, tracing, and logging for applications and distributed services.\nExperience integrating services with business systems through secure APIs, with working knowledge of identity, secrets management, and access control.\nExperience with AWS (including EC2), CI/CD pipelines, and Git branching and code review practices.\nWorking knowledge of responsible AI, data privacy, security, and auditability practices.\nAbility to work independently on well-defined tasks, ask for guidance when needed, and collaborate with engineers, data owners, and business partners.\nStrong written and verbal communication skills, with the ability to explain technical work to technical and non-technical audiences.\nStrong analytical thinking and problem-solving ability, with a willingness to learn new tools and techniques quickly.\nPreferred Qualifications:\nMaster’s degree in Computer Science, Artificial Intelligence, Data Science, Mathematics, Statistics, Engineering, or a related technical discipline.\nExposure to the Databricks ecosystem, including Mosaic AI Agent Framework, Vector Search, MLflow tracing and evaluation, Databricks SQL, and Unity Catalog.\nHands-on exposure to agentic frameworks (such as LangGraph or similar), multi-agent coordination, and automated tool calling.\nExposure to GraphRAG, entity resolution, and graph query languages (such as Cypher, SPARQL, or Gremlin).\nFamiliarity with hybrid search, re-ranking, and embedding model evaluation.\nExperience building MCP servers or reusable tool and skill definitions for agents.\nUnderstanding of model selection, token and inference cost optimization, and embedding models.\nExperience with infrastructure as code (such as Terraform) and containerization (such as Docker).\nExperience with AI or data solutions in a regulated environment such as financial services, banking, lending, or insurance.\nHours of Work:\nWork hours will depend on the business hours of the time zone serviced. To the extent permitted by law, the Company may, in its sole discretion, change the work schedule to address business needs.\nPhysical Demands:\nWhile performing the duties of this job, the employee is frequently required to sit for extended periods; reach with hands and arms; and talk or hear. The employee is occasionally required to move about. The employee must occasionally lift and/or move up to twenty (20) pounds. Specific vision abilities required by this job include close vision and the ability to adjust focus.\nEEO:\nMariner Finance is an Equal Opportunity Employer and does not discriminate on the basis of race, color, religion, creed, sex, gender, gender identity or expression, marital status, age, religion, national origin, sexual orientation, familial or caregiver status, citizenship status, status as a victim of domestic violence, medical condition, genetic information, pregnancy, physical or mental disability, or status as a disabled or Vietnam era veteran. Employee must be able to perform the essential duties/functions of the position satisfactorily and, if requested, reasonable accommodations will be made to enable employees with disabilities to perform the essential duties/functions of their job, absent undue hardship. Drug/Alcohol/Smoke-free workplace. \nThis job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities that are required of the employee. Duties, responsibilities, and activities may change or new ones may be assigned at any time or without notice.","description_format":"text","description_chars":9857,"description_truncated":false,"requirements":{"experience_years_min":6,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":true},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Consumer Loans & Pawnshops"],"lifecycle":[{"event":"open","at":"2026-11-02T17:52:40Z"},{"event":"close","at":"2026-11-04T03:55:31Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 1","filings_12m":1,"filings_prev_12m":3,"green_card_filings_12m":0,"median_offered_wage_usd":132500,"route":null,"cap_exempt":false,"checked_at":"2026-10-29T16:27:21+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)"],"filings_for_role_12m":1}],"liveness":null,"pay":{"stated_usd_annual":150000,"is_top_pay":false},"html_url":"https://alion.io/job/mariner-finance-ai-engineer-iii","json_url":"https://alion.io/job/mariner-finance-ai-engineer-iii.json","meta":{"generated_at":"2026-11-06T03:45:58Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":6320,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-07T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":2256507},"rest":"https://alion.io/mcp/rest/get_company?id=2256507"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fmariner-finance-ai-engineer-iii"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fmariner-finance-ai-engineer-iii"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fmariner-finance-ai-engineer-iii"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/mariner-finance-ai-engineer-iii\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fmariner-finance-ai-engineer-iii"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3619630}},{"tool":"search_jobs","arguments":{"company":"marinerfinance.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3619630},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. Docs: https://alion.io/developers"}}