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# Lead AI Software Engineer

- **Company:** Insight Global
- **Location:** Harrison, NY, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automation of Tests, Microsoft Azure, Code Generation, Code Review, Continuous Integration, Data Stores, Software Debugging, Distributed Systems, Python (Programming Language), PostgreSQL, Machine Learning, Object-Oriented Software Development, Software Architecture, Systems Development Life Cycle, Secure Coding, Software Engineering, Software Systems, Data Streaming, TypeScript, Enterprise Data Management, ReactJS, Snowflake, Apache Spark, Spring-boot, Generative AI, Functional Dependencies, Build Management, Apache Kafka, Data Pipelines, Databricks, Programming Languages
- **Published:** October 16, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3451654211&tx=KP1515FFG&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

A Lead SDE role grounded in strong software engineering fundamentals, with the judgment and practical fluency to use AI effectively, responsibly, and safely across product development and the software delivery lifecycle * Strong experience designing, developing, testing, delivering, and operating production software products or platforms. * Strong foundation in software architecture, object-oriented or functional design, APIs, distributed systems, data stores, testing, secure development, and operational excellence. * Hands-on proficiency in one or more modern programming languages and frameworks. Experience with Java, Spring Boot, Python, TypeScript, React, or comparable technologies is relevant. * Experience building cloud-based solutions using AWS, Azure, or comparable platforms and working with modern development, deployment, and observability practices. * Ability to lead technical design discussions, compare approaches, document decisions, and guide implementation across multiple
components or services. * Demonstrated ability to use AI-assisted tools in software engineering work and to validate their output with appropriate technical rigor. * Working knowledge of modern AI concepts and application patterns sufficient to identify opportunities, understand limitations, and collaborate with specialists when deeper AI expertise is required. * Demonstrates effective use of AI-assisted development tools by appropriately delegating tasks, providing clear context and requirements, optimizing prompt efficiency, and understanding AI capabilities and limitations to maximize productivity. * Applies strong engineering judgment to validate AI-generated outputs, identify hallucinations, mitigate bias and errors, protect sensitive information, and maintain ownership of technical decisions, quality, security, and outcomes. * Ability to translate ambiguous business or product needs into practical technical solutions and iterate using evidence and feedback. * Strong
written and verbal communication skills, with the ability to build alignment across technical and non-technical stakeholders. * Ability to work independently, mentor others, surface risks early, and deliver high-quality outcomes in a rapidly changing R&D environment. * Experience prototyping or delivering software products that integrate generative AI, machine learning services, retrieval, intelligent search, natural-language interfaces, or agent-enabled workflows. * Experience with enterprise data platforms or streaming and analytics technologies such as Databricks, Snowflake, Spark, Kafka, Postgres, or similar technologies. * Experience developing evaluation, testing, monitoring, or fallback approaches for AI-enabled application behavior. * Experience introducing emerging technologies or new engineering practices into a team while balancing experimentation with security, compliance, maintainability, and production readiness. * Experience working in payments,
financial services, commerce, or another highly regulated environment.

## Description

We are looking for a Lead Software Development Engineer with strong software engineering fundamentals and AI fluency. You will design, build, test, and deliver scalable products and platform capabilities in a fast-moving R&D environment. You will apply sound engineering judgment, guide technical decisions, and use AI-enabled tools and capabilities where they improve speed, quality, learning, or customer value. This is a software engineering role, not a model-research or data-science role. Success requires deep ownership of architecture, implementation, security, reliability, and operational outcomes, together with the ability to collaborate with AI effectively and critically.

 * Own the design and delivery of complex software capabilities from early technical discovery and prototyping through production readiness and operation.
 * Translate customer, product, and business intent into clear technical approaches, implementation plans, interfaces, and measurable outcomes.
 * Design and build secure, scalable, maintainable applications, APIs, services, data pipelines, and user experiences across cloud and enterprise environments.
 * Make and communicate architecture decisions, technical trade-offs, and risk assessments across services, systems, and teams.
 * Rapidly prototype and validate emerging technologies while establishing a credible path from experiment to production.
 * Use AI-assisted engineering tools to support activities such as discovery, design exploration, coding, testing, debugging, documentation, and code review, while remaining accountable for the resulting work.
 * Create clear specifications and sufficient context so that both people and AI tools can contribute effectively to engineering tasks.
 * Critically review AI-generated code, tests, explanations, and recommendations for correctness, security, maintainability, architectural fit, and business intent.
 * Identify where AI-enabled capabilities can improve a product or workflow, and recognize when conventional software approaches are more appropriate.
 * Apply responsible AI, data protection, intellectual property, security, and enterprise governance expectations when using or integrating AI capabilities.
 * Establish appropriate validation, testing, observability, and human review for software that incorporates probabilistic or AI-enabled behavior.
 * Drive engineering quality through code reviews, automated testing, CI/CD, production-readiness practices, troubleshooting, root-cause analysis, and operational improvement.
 * Partner effectively with product, design, data, architecture, security, and engineering stakeholders to build alignment and resolve cross-functional dependencies.
 * Mentor engineers, share reusable practices, and help the team strengthen both core engineering capability and practical AI fluency.