Lead AI Engineer

Role Summary

Alight is seeking a Lead AI Engineer to design, build and operationalize enterprise AI solutions for Business Health, Commercial and Finance. Based in India, this hands-on technical leadership role will improve forecasting, planning, performance visibility and executive decision support.

You will combine structured commercial and financial data with enterprise knowledge to create predictive models, AI agents, retrieval solutions and automated workflows that help leaders understand performance, anticipate risk and act faster.

Key Opportunity

• Improve forecast accuracy and risk visibility through predictive models, opportunity-movement analysis and scenario planning.
• Support bookings, territory, quota and capacity planning using pipeline coverage, conversion, deal-size and sales-cycle assumptions.
• Create Business Health scorecards and executive narratives across plan, forecast, pipeline, bookings, renewals, client risk, cost and margin.
• Automate recurring Commercial and Finance analysis, MBR/QBR preparation and action tracking.
• Build pricing, comparable-customer, market and closed-lost analytics for renewals, pursuits and account planning.
• Connect Salesforce, Clari, Power BI and enterprise content through governed GenAI, RAG and agentic workflows.
• Establish traceable, secure and evaluated AI for commercially and financially significant decisions.

• 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.

• 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.