Overview

Alight is seeking a Lead AI Engineer to design, build and operationalize enterprise AI solutions for Business Health, Commercial and Finance.

Responsibilities

• Design, build, test, deploy and continuously improve enterprise GenAI, agentic AI and machine-learning solutions.
• Partner with business and technology leaders to identify and prioritize AI opportunities based on value, feasibility, risk and expected outcomes.
• Establish success measures and value hypotheses, then use results to guide iteration, scaling or retirement decisions.
• Architect scalable LLM applications, AI agents, RAG systems, predictive solutions and supporting data and integration patterns.
• Develop high-quality Python applications, APIs, microservices, integrations and reusable components.
• Design and optimize ingestion, chunking, embeddings, semantic and hybrid search, vector retrieval, grounding and knowledge-management patterns.
• Build AI agents that securely use enterprise data, tools, APIs, memory, orchestration and human-in-the-loop controls.
• Define evaluation approaches for accuracy, relevance, groundedness, safety, robustness, latency, scalability and business effectiveness.
• Implement CI/CD, observability, logging, monitoring, incident response, versioning, and performance and cost optimization.
• Apply security, privacy, governance, explainability, auditability and human oversight throughout the solution lifecycle.
• Create reference architectures, reusable patterns, coding standards, documentation expectations and AI engineering best practices.
• Lead technical workstreams, conduct architecture and code reviews, mentor engineers, and communicate decisions, risks, trade-offs and results.

Qualifications

• 6+ years in software engineering, data engineering, AI/ML engineering or a related discipline, supported by a bachelor’s degree in a relevant field or equivalent practical experience.
• Demonstrated experience designing, building and deploying GenAI, agentic AI, RAG or machine-learning solutions tied to defined outcomes.
• Strong understanding of machine learning algorithms and time-series modelling, with hands-on experience in forecasting, trend analysis, feature engineering, model training, evaluation, deployment and monitoring across the ML lifecycle.
• Strong knowledge of LLMs, prompt and context engineering, tool calling, orchestration, memory, multi-agent patterns and human-in-the-loop design.
• Experience with embeddings, vector databases, semantic or hybrid search, document processing, knowledge retrieval and grounded generation.
• Advanced Python skills and experience building production APIs, microservices, integrations, automated tests and maintainable software components.
• Experience deploying AI solutions on AWS, Azure or GCP using containers, Git, CI/CD, logging and monitoring.
• Experience evaluating and monitoring AI systems across quality, reliability, safety, security, privacy, model risk, performance, cost and business impact.
• Strong system-design, problem-solving, collaboration and communication skills.
• AWS Bedrock, SageMaker or comparable enterprise AI services.
• LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen or equivalent frameworks.
• Commercial and open-source model ecosystems.
• Complex, security-conscious or regulated enterprise environments.
• Reusable AI platforms, accelerators or reference architectures.