Role Focus:Generative AI Engineering and Scaled AI Transformation for Source to Pay technology group - Hybrid1. Large Language Model (LLM) Strategy & Technical AuthorityActs as asenior technical authorityon Large Language Models, including bothcommercial and open‑source ecosystems(OpenAI, Gemini, Claude, Llama).Leadsmodel selection and deployment strategy , balancing use‑case fit, data sensitivity, cost efficiency, latency, accuracy, and regulatory constraints.Guides decisions onhosted vs. private vs. fine‑tuned models , ensuring optimal trade‑offs between performance, control, and operational risk.Establishesenterprise standards for LLM lifecycle management , including upgrades, regression validation, and decommissioning.2. Hands‑On GenAI Application & Agentic System DesignDemonstrateshands‑on leadershipin building GenAI applications usingLangChain, LangGraph, LlamaIndex, and Hugging Face , translating experimentation into production systems.Architectsagentic and multi‑step workflows , enabling tool‑use, reasoning chains, state management, and orchestration at enterprise scale.Sets reusablereference patterns and acceleratorsfor GenAI adoption across application teams.Ensures solutions are built withenterprise-grade reliability, explainability, and extensibility .3. Retrieval Augmented Generation (RAG) & Enterprise Knowledge EnablementDesigns and deliversrobust RAG architecturesthat ground GenAI outputs in trusted, auditable enterprise data.Leads implementation ofvector databases and embedding strategies(pgvector, Pinecone, Weaviate, FAISS), aligned with data access and security models.Appliesadvanced retrieval techniquesincluding hybrid search, re‑ranking, metadata filtering, and context optimization to improve response accuracy and relevance.Ensures RAG solutions supportdata lineage, auditability, and regulatory compliance .4. Prompt Engineering, Workflow Optimization & Cost ControlEstablishesprompt engineering and orchestration standardsto ensure consistency, maintainability, and quality across GenAI solutions.Optimizes GenAI workflows by actively managinglatency, throughput, token cost, and accuracy trade‑offsin production environments.Implementsevaluation and experimentation frameworksto continuously improve output quality and business value.Drives disciplined use of caching, batching, fallback models, and token optimization techniques.5. Machine Learning & Model Enablement FoundationsApplies strong grounding inML/DL fundamentals , enabling informed architectural decisions and credible engagement with data science teams.LeveragesPyTorch and TensorFlowfor embeddings, training pipelines, and targeted fine‑tuning where business value is clear.Ensures GenAI capabilities integrate seamlessly into the broaderML, data, and MLOps ecosystem .Balances rapid GenAI delivery with long‑term model sustainability and governance.6. Production Deployment, Scalability & Operational ExcellenceLeads deployment of GenAI systems intosecure, scalable production environmentsusingDocker, cloud‑native architectures, and hardened APIs .Establishesobservability and monitoringfor GenAI applications, covering performance, drift, quality, reliability, and failure modes.Ensures GenAI platforms meetenterprise availability, resilience, and disaster recovery expectations .Drives operational readiness, incident management, and ongoing optimization of AI services.7. Software Engineering LeadershipBrings stronghands‑on software engineering credibility , setting standards for Python‑based GenAI services.Leads development ofhigh‑performance AI‑powered APIsusing FastAPI and async programming patterns.Champions clean architecture, testability, and security best practices across AI engineering teams.Acts as a bridge betweentraditional application engineering and AI‑native development .8. AI Safety, Evaluation & Responsible AI GovernanceLeads the implementation ofAI evaluation and governance frameworks , including hallucination detection, confidence scoring, and human‑in‑the‑loop validation.Designs and enforcesguardrails, moderation layers, and usage controlsto prevent misuse or unintended outcomes.Partners with Risk, Compliance, Legal, and Security teams to embedResponsible AI principlesinto all GenAI solutions.Ensures GenAI adoption withstandsaudit, regulatory, and reputational scrutiny .9. Leadership, Influence & ExecutionOperates as ahands‑on SVP , combining strategic influence with deep technical execution.Leads senior engineers and GenAI specialists, buildingsustainable internal AI capabilityrather than point solutions.Communicates complex GenAI concepts clearly toexecutive and non‑technical stakeholders .Drives delivery inagile, fast‑moving environments , with a strong bias for outcomes and measurable value.Recommended Qualifications10+ years of progressive experiencein software engineering, ML, or AI platforms, with5+ years leading senior engineers and architects .3+ years of hands‑on experience deploying LLM‑based systemsin production environments at enterprise scale.Demonstrated authority acrosscommercial and open‑source LLM ecosystems(e.g., OpenAI, Anthropic, Google, Llama), including model selection, fine‑tuning, and hosting strategies.Proven ability to defineenterprise‑wide GenAI standards , reference architectures, and reusable accelerators.Demonstrated leadership in establishingprompt engineering standards and orchestration patterns .Experience optimizinglatency, throughput, accuracy, and token costacross large‑scale GenAI workloads.EducationBachelor’s degree/University degree or equivalent experienceMaster’s degree preferredCiti is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review.Accessibility at Citi. 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