Lead Software Engineer – AI Engineering

Overview

As a Software Engineering Lead, you will guide a skilled team to deliver secure, production-grade AI-driven software. You’ll shape technical vision with product and business partners, translating requirements into robust software and AI specifications. You thrive in an Agile setting, driving timely delivery and scalable solutions. You’ll foster innovation, accountability, and collaboration across a global organization, promoting responsible AI practices. This role offers impact at scale within a leading financial services firm and exposure to cutting-edge AI-enabled engineering.

Responsibilities

• Execute innovative software solutions and tackle complex design and debugging tasks
• Lead a local software/AI team, driving accountability and engagement
• Collaborate with product and business teams to set technical vision and roadmaps for AI-driven solutions
• Translate requirements into robust software and AI specifications, define milestones, and ensure timely delivery using Agile methods
• Architect and develop secure, high‑quality production software systems using Java or Python with AI/ML integrations
• Identify automation opportunities to improve reliability and operational efficiency
• Design experiments, implement algorithms, and productionize scalable AI/ML solutions
• Promote enterprise-approved AI-assisted engineering practices and standardized validation
• Ensure secure coding practices, peer reviews, and automated testing across the SDLC
• Apply and leverage AI-assisted development tools within the SDLC toolchain

Key requirements

• 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 development
• Experience integrating AI/ML techniques into software systems (LLMs, Generative AI, NLP, RAG, AI evals)
• Experience managing and mentoring software engineering or AI/ML teams
• Proven use of approved AI-assisted development tools and ensuring AI output quality
• Strong understanding of responsible AI use, data handling, and security requirements
• Knowledge of data structures, algorithms, and practical ML frameworks (TensorFlow, PyTorch, Scikit-Learn)
• Proficiency in automation, CI/CD, and cloud-native development (AWS)
• Hands-on experience delivering system design, application development, testing, and stability

• leadership and people management
• collaboration across cross-functional teams
• communication of complex technical concepts to non-technical stakeholders
• Java
• Python
• AI/ML integration in production systems

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