AI Full Stack Engineering Lead

About the role

This job is responsible for defining and leading the engineering approach for complex features to deliver significant business outcomes. Key responsibilities of the job include delivering complex features and technology, enabling development efficiencies, providing technical thought leadership based on conducting multiple software implementations, and applying both depth and breadth in a number of technical competencies.

Responsibilities

• Lead end-to-end delivery of AI and non-AI software solutions across multiple projects
• Design and implement scalable architectures (microservices, cloud-native, event-driven)
• Build and maintain backend systems using Java and Python
• Develop and integrate AI solutions (LLMs, RAG, agents, ML pipelines) into enterprise platforms
• Translate business requirements into technical designs and high-quality implementations
• Ensure adherence to enterprise standards for security, compliance, and performance
• Drive code quality, testing, and engineering best practices across teams
• Implement and manage CI/CD pipelines, monitoring, and production readiness
• Provide technical leadership and mentorship to engineers and review solution designs
• Collaborate with stakeholders to align technology delivery with business goals
• Create and review technical design documents, architecture diagrams, and standards
• Drive reusability, modularity, and scalability across solutions
• Design and manage data ingestion, transformation, and processing pipelines
• Work with structured and unstructured data, including financial and operational datasets
• Implement feature engineering, model deployment, and monitoring pipelines

Required Qualifications

• 10+ years of experience in software engineering and system design
• Proven experience delivering large-scale enterprise applications and AI solutions
• Strong expertise in: Java (Spring Boot, Microservices), Python (AI/ML, APIs, data engineering)
• Hands-on experience with: AI/ML frameworks (OpenAI, Hugging Face, LangChain, etc.), RAG pipelines, embeddings, vector databases, RESTful APIs, distributed systems
• Deep understanding of: Microservices, APIs, event-driven architectures, Cloud platforms (Azure preferred), Containerization (Docker, Kubernetes)
• Practical experience with: LLM-based applications and prompt engineering, Model lifecycle management (training, deployment, monitoring), AI governance, risk, and explainability (preferred in regulated industries)
• Strong problem-solving and analytical thinking
• Excellent communication and stakeholder management
• Ability to operate in a fast-paced, ambiguous environment

Desired Qualifications

• Experience in financial services or regulated industries
• Exposure to Microsoft ecosystem (Copilot Studio, Foundry, Fabric)
• Familiarity with agent orchestration, MCP, and AI platform integration patterns
• Experience with data privacy, compliance, and secure AI deployments

Skills

• Automation
• Influence
• Result Orientation
• Stakeholder Management
• Technical Strategy Development
• Application Development
• Architecture
• Business Acumen
• Risk Management
• Solution Design
• Agile Practices
• Analytical Thinking
• Collaboration
• Data Management
• Solution Delivery Process