Senior Software Engineer

Cotality•Dallas, TX

1d•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 AI, automation, and enterprise software solutions to transform business operations, improve efficiency, and support scalable digital modernization for the Transformation team. It applies systems thinking, AI-assisted development, specification-driven delivery, automated validation, and production engineering practices to deliver secure, scalable, reliable, high-quality solutions. The engineer builds full-stack software and AI-enabled capabilities, including interfaces, APIs, services, workflows, data integrations, cloud-native components, LLM integrations, retrieval pipelines, and agentic workflows. The role translates product intent and technical specifications into validated production implementations, establishes evaluation and feedback mechanisms for AI quality and risk, and owns testing and operational readiness for assigned work. The engineer collaborates with cross-functional teams and applies responsible AI, security, privacy, accessibility, performance, resilience, and maintainability standards.

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, such as applications, platforms, internal tools, or developer infrastructure.
Strong programming skills across multiple languages and technology stacks, with the ability to work across frontend, backend, infrastructure, and DevOps as needed.
Required full-stack expertise in Java and Angular.
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 in computer science, Software Engineering, or a related field preferred.
Additional skills in Python, JavaScript, TypeScript, PHP, Spring Boot, React, Spring Security, Hibernate, and Node.js are desirable but not essential.

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 that connect 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 assess 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 with 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 assess 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.
Drive adoption of new tools, practices, or platforms across engineering organizations, including building solutions and enabling teams to use them effectively.
Provide technical leadership through architectural guidance, code reviews, and hands-on pairing.
Communicate technical information to leadership and collaborate directly with engineers.

Benefits

Generous PTO and 11 paid holidays, plus well-being and volunteer time off.
Up to 16 weeks of fully paid parental leave and a baby stipend.
Multiple medical plan options with mental health and wellness support offerings.
401(k) with company match and vesting after one year.
$400 annual well-being stipend.
Tuition assistance up to $5,250.
Recognition Rewards, referral bonuses, exclusive discounts, and other perks.
Flexible working model.
Competitive time off and health coverage.
Growth opportunities.