Senior Software Engineer

CoreLogic•Dallas, TX

4d•$111,900 - $156,700•Hybrid

About The Position

A Senior Professional, Software Engineer designs, builds, tests, deploys, and operates end-to-end product capabilities within an AI-native engineering pod. The role develops and delivers AI, automation, and enterprise software solutions that transform business operations, improve efficiency, and enable scalable digital modernization. The engineer applies systems thinking, AI-assisted development, specification-driven delivery, automated validation, and strong production engineering practices to deliver secure, scalable, reliable, and high-quality solutions for the Transformation team. The position works across full-stack development, AI-enabled services, enterprise integrations, evaluation and validation, production operations, and responsible AI. It involves collaboration with engineering, product, business, TPM, architecture, data, security, operations, and domain teams to deliver technology solutions aligned with compliance expectations and measurable business impact.

Requirements

Bachelor’s degree in computer science, Software Engineering, or a related field.
At least 3 years of software development experience, with demonstrated ability to lead technical initiatives across teams.
Strong understanding of the full software development lifecycle, from requirements and architecture through build, test, and deployment.
Extensive experience designing and implementing scalable systems, including applications, platforms, internal tools, or developer infrastructure.
Strong programming skills across multiple languages and technology stacks, with a broad generalist mindset and comfort working across frontend, backend, infrastructure, and DevOps as needed.
Expertise in full-stack development using Java and Angular.
Experience driving adoption of new tools, practices, or platforms across engineering organizations, including building solutions and enabling teams to use them effectively.
Familiarity with AI-assisted development tools and workflows such as GitHub Copilot, LLM APIs, agent frameworks, or similar.
Strong software fundamentals, including programming, data structures, algorithms, APIs, distributed systems, cloud services, secure coding, testing, debugging, and operational readiness.
Practical AI-native delivery experience, including context engineering, prompt engineering, AI-assisted coding, agent orchestration, retrieval quality, model evaluation, output validation, and responsible AI practices.
Exceptional communication skills and ability to present effectively to leadership and collaborate directly with engineers.
Strong analytical thinking and ability to assess technical opportunities, evaluate trade-offs, and make architectural decisions in ambiguous, fast-moving environments.

Nice To Haves

Advanced degree.
Experience with Python, JavaScript, TypeScript, PHP, Spring Boot, React, Spring Security, Hibernate, or Node.js.

Responsibilities

Build full-stack capabilities across user interfaces, APIs, services, workflows, data integrations, and cloud-native components.
Design and implement AI-enabled services, LLM integrations, retrieval pipelines, semantic search, embeddings, classification, ranking, workflow automation, and decision-support capabilities.
Design prompt architectures, system instructions, tool-use patterns, memory and context strategies, agentic workflows, orchestration patterns, and human-in-the-loop approval paths.
Engineer context pipelines connecting enterprise knowledge, product specifications, domain rules, data quality signals, and user intent into reliable model inputs.
Translate product intent, technical specifications, and acceptance criteria into validated, production-ready implementation.
Build evaluation harnesses, test datasets, quality thresholds, and feedback loops to measure accuracy, groundedness, completeness, consistency, usefulness, latency, cost, reliability, safety, and risk.
Own automated testing, CI/CD readiness, observability, auditability, traceability, incident response, rollback planning, and production validation for assigned scope.
Collaborate across engineering, product, business, TPM, architecture, data, security, operations, and domain teams to design, build, and deploy technology solutions aligned with AI behavior, software delivery, compliance expectations, and measurable business impact.
Use AI coding assistants, reusable context, specification-driven delivery, automated validation, and agentic workflows to generate, refactor, test, and document software responsibly.
Apply systems thinking to understand upstream and downstream impacts, cross-service dependencies, user workflows, data flows, security boundaries, operational failure modes, compliance obligations, and long-term maintainability.
Identify and address responsible AI risks, including hallucination, bias, privacy exposure, data leakage, insufficient explainability, unsafe automation, and limited human validation.
Maintain security, privacy, accessibility, performance, resilience, and maintainability standards across delivered work.
Provide technical leadership through architectural guidance, code reviews, and hands-on pairing.