{"id":2575679,"url":"https://alion.io/job/exl-gen-ai-engineering-lead","title":"Gen AI - Engineering Lead","company":{"id":38016,"name":"EXL","domain":"exlservice.com","url":"https://alion.io/company/exl","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"B","score":82,"open_postings":68,"ghost_share":0,"stale_share":0.926,"repost_share":0,"time_to_fill_p50_days":8,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":144000,"max_usd":294000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":691},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Cortex","optional":false},{"name":"CrewAI","optional":false},{"name":"Embeddings","optional":false},{"name":"GCP","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"Snowflake","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Vertex AI","optional":false},{"name":"Prometheus","optional":true}],"status":"live","first_seen_at":"2026-10-13T05:56:29Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-17T15:46:53Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"We are seeking a highly skilled Senior Generative AI Engineer Lead to drive the design, development, and deployment of enterprise-grade Generative AI solutions. The ideal candidate will have deep expertise in Large Language Models (LLMs), prompt engineering, AI orchestration frameworks, cloud-native AI architectures, and model evaluation methodologies.\nThis role will lead the end-to-end lifecycle of GenAI initiatives, from translating business requirements into AI-powered prototypes to delivering scalable, production-ready solutions. The candidate will also establish engineering best practices around prompt governance, model guardrails, benchmarking, and performance optimization.\n Generative AI Solution Design\nArchitect and implement enterprise-scale GenAI solutions using LLMs, foundation models, and agentic AI frameworks.\nDesign reusable AI patterns, accelerators, and reference architectures to enable rapid solution development.\nTranslate business problems into scalable AI workflows and production-ready proof-of-concepts.\nDrive AI platform modernization through adoption of emerging GenAI technologies and best practices.\nPrompt Engineering & LLM Governance\nDevelop and maintain sophisticated prompt engineering frameworks for controlled and reliable LLM outputs.\nImplement prompt versioning, prompt lifecycle management, and testing strategies.\nDesign AI guardrails to mitigate hallucinations, bias, security risks, and compliance concerns.\nEstablish best practices for prompt optimization, response consistency, and output quality management.\nAI Workflow Orchestration & Automation\nDesign and build scalable orchestration pipelines using frameworks such as LangGraph, LangChain, CrewAI, Semantic Kernel, or equivalent.\nDevelop reusable AI components, tools, agents, and workflow templates for enterprise adoption.\nImplement multi-agent systems and autonomous workflows to support complex business use cases.\nPrototyping & Business Enablement\nPartner with business stakeholders to identify high-value AI opportunities.\nRapidly develop AI prototypes and MVP solutions using synthetic and enterprise datasets.\nConvert prototypes into production-ready applications adhering to scalability, security, and reliability standards.\nCloud & Data Engineering\nBuild scalable GenAI architectures across AWS, GCP, and Snowflake platforms.\nLeverage cloud-native AI services including Amazon Bedrock, SageMaker, Vertex AI, Snowflake Cortex AI, and related ecosystems.\nDesign robust RAG (Retrieval-Augmented Generation) architectures incorporating vector databases, embeddings, and semantic search.\nOptimize model deployment, inference performance, and infrastructure cost efficiency.\nEvaluation & Performance Optimization\nEstablish AI evaluation frameworks to measure accuracy, relevance, latency, safety, and business impact.\nDevelop benchmarking methodologies for comparing prompts, models, and workflows.\nDefine KPIs, observability frameworks, and monitoring strategies for GenAI applications.\nContinuously improve model performance through prompt tuning, retrieval optimization, and workflow enhancements.\n Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field. \n8+ years of experience in software engineering, machine learning, data engineering, or AI solution development. \n4+ years of hands-on experience with Generative AI, LLMs, and foundation models. \nStrong experience designing enterprise GenAI solutions utilizing advanced LLM architectures and prompt frameworks. \nExpertise in building scalable AI workflows, orchestration pipelines, and reusable AI components.\nProven ability to translate business requirements into production-ready AI solutions and prototypes using synthetic data. \nDeep understanding of prompt versioning, prompt governance, guardrails, and controlled LLM outputs. \nStrong knowledge of AI evaluation frameworks, benchmarking methodologies, and optimization techniques. \nExperience with vector databases, embeddings, semantic search, and RAG architectures. \nStrong proficiency in Python and modern AI/ML development 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