{"id":3214038,"url":"https://alion.io/job/alight-solutions-gen-ai-lead","title":"Gen AI lead","company":{"id":1929,"name":"Alight Solutions","domain":"alight.com","url":"https://alion.io/company/alight-solutions","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Kochi, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":55000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":47},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Context Engineering","optional":false},{"name":"CrewAI","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Hybrid Search","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Time Series","optional":false}],"status":"live","first_seen_at":"2026-10-24T17:28:48Z","employer_posted_date":null,"last_verified_at":"2026-10-24T17:28:48Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"Role Summary\n\nAlight 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.\n\nYou 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.\n\nKey Opportunity\n\nImprove forecast accuracy and risk visibility through predictive models, opportunity-movement analysis and scenario planning.\nSupport bookings, territory, quota and capacity planning using pipeline coverage, conversion, deal-size and sales-cycle assumptions.\nCreate Business Health scorecards and executive narratives across plan, forecast, pipeline, bookings, renewals, client risk, cost and margin.\nAutomate recurring Commercial and Finance analysis, MBR/QBR preparation and action tracking.\nBuild pricing, comparable-customer, market and closed-lost analytics for renewals, pursuits and account planning.\nConnect Salesforce, Clari, Power BI and enterprise content through governed GenAI, RAG and agentic workflows.\nEstablish traceable, secure and evaluated AI for commercially and financially significant decisions.\n\nResponsibilities\n\nDesign, build, test, deploy and continuously improve enterprise GenAI, agentic AI and machine-learning solutions.\nPartner with business and technology leaders to identify and prioritize AI opportunities based on value, feasibility, risk and expected outcomes.\nEstablish success measures and value hypotheses, then use results to guide iteration, scaling or retirement decisions.\nArchitect scalable LLM applications, AI agents, RAG systems, predictive solutions and supporting data and integration patterns.\nDevelop high-quality Python applications, APIs, microservices, integrations and reusable components.\nDesign and optimize ingestion, chunking, embeddings, semantic and hybrid search, vector retrieval, grounding and knowledge-management patterns.\nBuild AI agents that securely use enterprise data, tools, APIs, memory, orchestration and human-in-the-loop controls.\nDefine evaluation approaches for accuracy, relevance, groundedness, safety, robustness, latency, scalability and business effectiveness.\nImplement CI/CD, observability, logging, monitoring, incident response, versioning, and performance and cost optimization.\nApply security, privacy, governance, explainability, auditability and human oversight throughout the solution lifecycle.\nCreate reference architectures, reusable patterns, coding standards, documentation expectations and AI engineering best practices.\nLead technical workstreams, conduct architecture and code reviews, mentor engineers, and communicate decisions, risks, trade-offs and results.\n\nRequired Qualifications\n\n6+ 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.\nDemonstrated experience designing, building and deploying GenAI, agentic AI, RAG or machine-learning solutions tied to defined outcomes.\nStrong 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.\nStrong knowledge of LLMs, prompt and context engineering, tool calling, orchestration, memory, multi-agent patterns and human-in-the-loop design.\nExperience with embeddings, vector databases, semantic or hybrid search, document processing, knowledge retrieval and grounded generation.\nAdvanced Python skills and experience building production APIs, microservices, integrations, automated tests and maintainable software components.\nExperience deploying AI solutions on AWS, Azure or GCP using containers, Git, CI/CD, logging and monitoring.\nExperience evaluating and monitoring AI systems across quality, reliability, safety, security, privacy, model risk, performance, cost and business impact.\nStrong system-design, problem-solving, collaboration and communication skills.\nAWS Bedrock, SageMaker or comparable enterprise AI services.\nLangGraph, LangChain, LlamaIndex, CrewAI, AutoGen or equivalent frameworks.\nCommercial and open-source model ecosystems.\nComplex, security-conscious or regulated enterprise environments.\nReusable AI platforms, accelerators or reference architectures.\n\nWe offer you a competitive total rewards package, continuing education & training, and tremendous potential with a growing worldwide 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