Who We Are
Xactus (pronounced ‘Zac-tus’) is the leading verification innovator for the mortgage industry. We have over 6,500 clients ranging from the largest bank and non-bank mortgage originators to credit unions and mortgage brokers. Xactus works closely with our clients to digitally integrate a 360° approach to verification across their workflows. As a result, lenders can easily access the technology necessary to meet consumer demands for a modern mortgage experience with industry-leading speed, reliability, and accuracy – while also closing more loans more quickly with greater profitability.
Xactus is proud to provide a friendly work environment that is primarily remote. Our workforce provides many opportunities for you to enhance your skills with our top-notch financial leadership team who prioritizes building talent. If you are curious and searching for a change that will be fun and rewarding, please reach out to us! We would love to have you on our team!
Who You Are
You are a career-minded, driven individual who is looking for a position that challenges you and supports your professional development.
The Benefits We Offer
A friendly, supportive environment which is highly rated by Xactus employees. Feedback from our employees says: “The people I work with treat each other with respect,” “I feel accepted by my coworkers,” and “The person I report to cares about me as a person.”
Xactus offers medical, vision and dental insurances, bonus programs, fitness reimbursement and other healthy life-style programs through our benefits carrier, 401k plan with a company match, short and long-term disability, life insurance, accident and critical illness insurance, health savings account, flexible spending account, employee assistance program, legal services, employee discounts and more.
Summary
The AI Engineer (GenAI Features) specializes in designing, building, and deploying production-grade generative AI applications. This role focuses on translating GenAI concepts into customer-facing and internal features using modern LLM frameworks (LangChain, LangSmith, Agentcore) and advanced retrieval architectures (RAG, GraphRAG with Neo4j/Neptune). The AI Engineer collaborates closely with DevOps and data engineering teams to architect end-to-end solutions, write custom ETL logic, and optimize retrieval systems. Success requires deep expertise in GenAI patterns, knowledge graph design, semantic search, and the ability to bridge research and production while maintaining code quality and system reliability.
Essential Functions
The following is a list of essential functions, which is subject to change at any time and without advance notice. Management may assign new duties, reassign existing duties, or eliminate a function based on business needs or at its sole discretion.
Essential Duties And Responsibilities
- GenAI Feature Development: Build production GenAI applications using LangChain, LangSmith, and Agentcore. Implement complex agent workflows with tool use, memory management, and multi-step reasoning. Design and optimize agentic patterns for reliability, latency, and cost.
- RAG & GraphRAG Architecture: Design and implement retrieval-augmented generation systems using semantic search and vector databases. Architect knowledge graphs using Neo4j/Neptune for structured reasoning. Optimize retrieval strategies for accuracy, latency, and relevance ranking.
- Data Pipeline & ETL Development: Write custom Python ETL scripts for data preparation, indexing, and synchronization. Design schema and data models for graph databases. Collaborate with data team on AWS Glue orchestration and S3 management.
- Database & Query Optimization: Write optimized SQL queries and design database schema. Optimize Neo4j/Neptune Cypher/SPARQL queries and implement efficient vector similarity search in OpenSearch.
- Backend API Development: Build production FastAPI services for GenAI features. Implement async request handling, error management, and fallback strategies for LLM unreliability.
- Model Tracking & Experimentation: Establish experiment tracking using MLflow, including model versioning, hyperparameter logging, and metrics comparison. Log and monitor LLM outputs for quality and bias.
- Production Monitoring & Debugging: Implement logging, metrics, and tracing using CloudWatch and LangSmith. Monitor LLM latency, token usage, and costs. Respond to production incidents with rapid remediation.
- Code Quality & Testing: Write unit, integration, and end-to-end tests. Perform code reviews and test AI system outputs for correctness and safety.
- Cross-Functional Collaboration: Partner with DevOps to deploy GenAI services. Communicate with data engineering on pipeline needs. Work with product to translate requirements into technical designs.
- Documentation & Knowledge Sharing: Document system architectures, RAG/GraphRAG designs, and operational runbooks. Share learnings on agent patterns and prompt engineering.
Qualifications
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required.
Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Education And/Or Experience
- Bachelor’s degree in related field or equivalent
- 3–5 years building production AI/ML systems or full-stack applications. Experience with ML/AI frameworks (PyTorch, TensorFlow, scikit-learn). 1–2+ years with LangChain, LangSmith, or similar LLM frameworks. Hands-on experience with Claude and/or GPT models. 2+ years of AWS experience (S3, CloudFormation, CloudWatch). Strong Python and FastAPI. SQL expertise. RAG and semantic search experience. Software engineering discipline (testing, code review, CI/CD).
- Highly Preferred: AWS Bedrock, Codex/code generation models, Prompt engineering at scale, GraphRAG/Neo4j/Neptune, Deep learning (CNNs, RNNs, transformers), NLP (tokenization, embeddings, text classification), Hyperparameter optimization, Computer vision/OCR, Terraform, Docker/Kubernetes, MLflow, OpenSearch, AWS Glue, Track record of shipping GenAI features.
Skills And Competentcies
- Strong written and verbal communication skills
- LLM Mastery: Deep understanding of Claude, GPT, and Codex capabilities, limitations, and failure modes.
- Problem Solving: Breaks complex AI problems into components; designs experiments to validate strategies.
- Production Reliability: Builds for failure, monitors proactively, responds rapidly to production issues.
- Cross-Functional Collaboration: Communicates effectively with DevOps, data engineers, and product teams.
Working Conditions
- Traditional office environment with low-to-moderate office noise (computers, phones and business conversations). The position may be remote from main offices.
- May require flexibility in hours.
Physical Demands
Xactus promotes an equal opportunity workplace, which includes reasonable accommodations of otherwise qualified disabled applicants and team members. Please contact your supervisor with questions regarding the physical demands of this position.
- Lifting/carrying up to 10 lbs.
- Manual dexterity for computer work
- Speaking, hearing and vision are required to perform essential functions.
Additional Information Regarding Employment With Xactus
If applying for a position at Xactus, candidates must be a resident of the United States, and should be authorized and eligible for employment in the United States. Xactus does not provide visa sponsorship at this time.
If hired to work at Xactus, employment is contingent upon successful completion of required background checks (including but not limited to federal and state criminal history checks, employment history verification, education verification, credit history check) and pre-employment drug screening.
No agency submissions, please. We do not accept unsolicited resumes from third-party recruiters.