Gen AI Engineering and Scaled AI Transformation

Role Focus:Generative AI Engineering and Scaled AI Transformation for Source to Pay technology group - Hybrid

1. Large Language Model (LLM) Strategy & Technical Authority

• Acts as asenior technical authority on 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 Design

• Demonstrateshands‑on leadership in 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 accelerators for GenAI adoption across application teams.
• Ensures solutions are built withenterprise-grade reliability, explainability, and extensibility.

3. Retrieval Augmented Generation (RAG) & Enterprise Knowledge Enablement

• Designs and deliversrobust RAG architectures that 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 techniques including 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 Control

• Establishesprompt engineering and orchestration standards to ensure consistency, maintainability, and quality across GenAI solutions.
• Optimizes GenAI workflows by actively managinglatency, throughput, token cost, and accuracy trade‑offs in production environments.
• Implementsevaluation and experimentation frameworks to continuously improve output quality and business value.
• Drives disciplined use of caching, batching, fallback models, and token optimization techniques.

5. Machine Learning & Model Enablement Foundations

• Applies strong grounding inML/DL fundamentals, enabling informed architectural decisions and credible engagement with data science teams.
• LeveragesPyTorch and TensorFlow for 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 Excellence

• Leads deployment of GenAI systems intosecure, scalable production environments usingDocker, cloud‑native architectures, and hardened APIs.
• Establishesobservability and monitoring for 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 Leadership

• Brings stronghands‑on software engineering credibility, setting standards for Python‑based GenAI services.
• Leads development ofhigh‑performance AI‑powered APIs using 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 Governance

• Leads 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 controls to prevent misuse or unintended outcomes.
• Partners with Risk, Compliance, Legal, and Security teams to embedResponsible AI principles into all GenAI solutions.
• Ensures GenAI adoption withstandsaudit, regulatory, and reputational scrutiny.

9. Leadership, Influence & Execution

• Operates as ahands‑on SVP, combining strategic influence with deep technical execution.
• Leads senior engineers and GenAI specialists, buildingsustainable internal AI capability rather 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 Qualifications:

• 10+ years of progressive experience in software engineering, ML, or AI platforms, with5+ years leading senior engineers and architects.
• 3+ years of hands‑on experience deploying LLM‑based systems in 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 cost across large‑scale GenAI workloads.

Education:

• Bachelor’s degree/University degree or equivalent experience
• Master’s degree preferred

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Automated Processing and AI

We use automated processing, including artificial intelligence, for our legitimate business interests (or our reasonable and appropriate business purposes) to identify and align the candidate's skills and abilities with a specific job opening. Additionally, if you so choose, or consent, we can match your skills and abilities to other suitable roles at Citi.

Importantly, all our hiring processes and decisions, including determining your suitability for a role, are conducted, checked, and decided by individuals. Our automated processing and AI do not involve relying on automatic or autonomous decision-making. Please refer to any Jurisdictional Considerations, with specific provisions for your country (where relevant) for further details.

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This job opening is for an existing job vacancy.

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