AI Lead Engineer

About the role

DXC Technology is a leading enterprise technology and innovation partner delivering software, services, and solutions to global enterprises and public sector organizations. You’ll directly shape how the world's leading insurers operate by helping to transform the policy, underwriting, and claims systems that millions of people rely on every day.

Responsibilities

• Understand the define technical vision, roadmap, and architecture for Generative AI and Agentic AI capabilities within the Assure Build Platform, ensuring scalability, security, and enterprise readiness.
• Lead the design and implementation of core GenAI platform components, including: - Agentic AI and multi-agent orchestration, Retrieval-Augmented Generation (RAG) pipelines, LLM model selection, configuration, and prompt tooling, Model fine-tuning and customization pipelines.
• Architect and govern AI agents capable of multi-step reasoning, tool usage, memory management, and workflow orchestration, ensuring reliability, traceability, and controlled execution.
• Drive LLM inference optimization initiatives, including: - Prompt engineering and prompt tuning, response and retrieval caching strategies, latency reduction and throughput optimization, cost governance across multiple model families.
• Lead integration of Copilot Studio-based agents into Assure workflows, translating interaction history and usage patterns into reusable, production-grade AI solutions.
• Partner closely with engineering, platform, product, and operations teams to embed GenAI capabilities into high-impact Assure workflows such as migration acceleration, testing automation, performance analysis, and operational intelligence.
• Establish and enforce best practices for GenAI engineering, including secure prompt handling, evaluation frameworks, monitoring, logging, and responsible AI principles.
• Mentor and guide senior and junior engineers, cultivating deep technical expertise in GenAI, agentic systems, and platform-first design.
• Promote a culture of innovation, experimentation, and learning, while maintaining strong governance, operational excellence, and long-term platform sustainability.

Minimum Qualifications

• 8+ years of professional software development experience building scalable, distributed, and maintainable systems.
• 3+ years of experience in a technical leadership role, guiding teams through complex architectural and design decisions and setting high standards for performance, reliability, and code quality.
• Deep, hands-on expertise in Large Language Models (LLMs), including: - Inferencing and runtime behavior, Embedding generation, Knowledge integration using Retrieval-Augmented Generation (RAG).
• Strong, demonstrated experience with Agentic AI systems, including: - Design and implementation of single-agent and multi-agent architectures, Multi-step reasoning and planning, Tool-calling and workflow orchestration, Agent memory, state management, and traceability, Human-in-the-loop and controlled execution patterns.
• Proven experience designing and delivering enterprise-grade GenAI platforms that support: - Agent orchestration and lifecycle management, Prompt engineering, versioning, and governance, RAG integration and evaluation, Model selection, configuration, and deployment.
• Experience fine-tuning, adapting, or customizing foundation models to improve task-specific performance and domain alignment.
• Advanced knowledge of LLM and Agent inference optimization techniques, including prompt tuning, caching strategies, quantization, and latency reduction across different model families.
• Strong programming skills in Python (mandatory); experience with Java or equivalent languages for platform and systems engineering is a strong plus.
• Demonstrated ability to work cross-functionally and influence product and platform direction through technical leadership and user-centered thinking.
• Strong passion for operational excellence, observability, automation, and building secure, developer-friendly AI and Agentic AI infrastructure at scale.
• Bachelor’s, Master’s, or PhD degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience.