Lead Software Engineer - AI Engineering

Join us to make a meaningful impact as you lead a talented team in building advanced AI-driven software solutions. You will have the opportunity to grow your career, collaborate globally, and influence the technical vision of our organization. We value your expertise in software engineering and AI/ML, and offer a dynamic environment where your ideas and leadership drive real change. Experience the benefits of working with cutting-edge technology and a supportive, inclusive team. Together, we push the boundaries of what’s possible.

As a Software Engineering Lead in our AI/ML Solutions Team, you will architect, design, and deliver secure, high-quality production software systems. You will collaborate with product and business teams to set technical vision and execute strategic roadmaps for AI-driven solutions. Your role involves translating business requirements into robust software and AI/ML specifications, ensuring timely delivery using Agile methodologies. You will foster a culture of innovation, accountability, and engagement within a global organization. Your leadership will help drive adoption of enterprise-authorized AI-assisted engineering practices and promote consistent validation standards across the team.

Job Responsibilities:

• Execute creative software solutions, design, development, and technical troubleshooting, thinking beyond conventional approaches.

• Develop secure, high-quality production code, review and debug code written by others.

• Lead a local team of software engineers and applied AI/ML practitioners, driving accountability and engagement.

• Collaborate with product and business teams to set technical vision and execute strategic roadmaps for AI-driven solutions.

• Translate business requirements into robust software and AI/ML specifications, define milestones, and ensure timely delivery using Agile methodologies.

• Architect, design, and develop secure, high-quality production software systems using Java or Python, integrating AI/ML techniques such as LLMs, Generative AI, and coding assistants.

• Identify opportunities to automate and remediate recurring issues, improving overall system reliability.

• Design experiments, implement algorithms, validate results, and productionize scalable and trustworthy AI/ML solutions.

• Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes.

• Establish consistent validation standards, including secure coding, peer review, automated testing, and promote reuse of effective patterns.

• Apply knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities.

Required Qualifications, Capabilities, and Skills:

• Formal training or certification in software engineering concepts.

• Familiarity with agentic workflows and frameworks (e.g., LangChain, LangGraph, Auto-GPT).

• Advanced proficiency in Java or Python for software system development; strong grasp of software engineering best practices, system design, application development, testing, and operational stability.

• Experience integrating AI/ML techniques into software systems, including familiarity with LLMs, Generative AI, NLP, RAG, AI evals, and coding assistants.

• Managing and mentoring software engineering or AI/ML teams, with experience as a hands-on practitioner delivering production-grade solutions.

• Demonstrated experience leading effective use of approved AI-assisted software development tools, with the ability to set team expectations for validating AI outputs for correctness, performance, and security.

• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption.

• Good understanding of data structures, algorithms, and practical machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn).

• Proficiency in automation, continuous delivery (CI/CD), and cloud-native development (preferably AWS).

• Hands-on practical experience delivering system design, application development, testing, and operational stability.

Preferred Qualifications, Capabilities, and Skills:

• Experience working at code level with advanced AI/ML business applications (e.g., LLMs, Generative AI, NLP).

• AWS Certifications (Solution Architect Associate or Professional) are advantageous.

• In-depth knowledge of the financial services industry and their IT systems.

• Practical cloud native experience.