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 a senior technical authority on Large Language Models, including both commercial and open‑source ecosystems (OpenAI, Gemini, Claude, Llama).
• Leads model selection and deployment strategy , balancing use‑case fit, data sensitivity, cost efficiency, latency, accuracy, and regulatory constraints.
• Guides decisions on hosted vs. private vs. fine‑tuned models , ensuring optimal trade‑offs between performance, control, and operational risk.
• Establishes enterprise standards for LLM lifecycle management , including upgrades, regression validation, and decommissioning.

2. Hands‑On GenAI Application & Agentic System Design

• Demonstrates hands‑on leadership in building GenAI applications using LangChain, LangGraph, LlamaIndex, and Hugging Face , translating experimentation into production systems.
• Architects agentic and multi‑step workflows , enabling tool‑use, reasoning chains, state management, and orchestration at enterprise scale.
• Sets reusable reference patterns and accelerators for GenAI adoption across application teams.
• Ensures solutions are built with enterprise-grade reliability, explainability, and extensibility .

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

• Designs and delivers robust RAG architectures that ground GenAI outputs in trusted, auditable enterprise data.
• Leads implementation of vector databases and embedding strategies (pgvector, Pinecone, Weaviate, FAISS), aligned with data access and security models.
• Applies advanced retrieval techniques including hybrid search, re‑ranking, metadata filtering, and context optimization to improve response accuracy and relevance.
• Ensures RAG solutions support data lineage, auditability, and regulatory compliance .

4. Prompt Engineering, Workflow Optimization & Cost Control

• Establishes prompt engineering and orchestration standards to ensure consistency, maintainability, and quality across GenAI solutions.
• Optimizes GenAI workflows by actively managing latency, throughput, token cost, and accuracy trade‑offs in production environments.
• Implements evaluation 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 in ML/DL fundamentals , enabling informed architectural decisions and credible engagement with data science teams.
• Leverages PyTorch and TensorFlow for embeddings, training pipelines, and targeted fine‑tuning where business value is clear.
• Ensures GenAI capabilities integrate seamlessly into the broader ML, 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 into secure, scalable production environments using Docker, cloud‑native architectures, and hardened APIs .
• Establishes observability and monitoring for GenAI applications, covering performance, drift, quality, reliability, and failure modes.
• Ensures GenAI platforms meet enterprise availability, resilience, and disaster recovery expectations .
• Drives operational readiness, incident management, and ongoing optimization of AI services.

7. Software Engineering Leadership

• Brings strong hands‑on software engineering credibility , setting standards for Python‑based GenAI services.
• Leads development of high‑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 between traditional application engineering and AI‑native development .

8. AI Safety, Evaluation & Responsible AI Governance

• Leads the implementation of AI evaluation and governance frameworks , including hallucination detection, confidence scoring, and human‑in‑the‑loop validation.
• Designs and enforces guardrails, moderation layers, and usage controls to prevent misuse or unintended outcomes.
• Partners with Risk, Compliance, Legal, and Security teams to embed Responsible AI principles into all GenAI solutions.
• Ensures GenAI adoption withstands audit, regulatory, and reputational scrutiny .

9. Leadership, Influence & Execution

• Operates as a hands‑on SVP , combining strategic influence with deep technical execution.
• Leads senior engineers and GenAI specialists, building sustainable internal AI capability rather than point solutions.
• Communicates complex GenAI concepts clearly to executive and non‑technical stakeholders .
• Drives delivery in agile, 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, with 5+ years leading senior engineers and architects .
• 3+ years of hands‑on experience deploying LLM‑based systems in production environments at enterprise scale.
• Demonstrated authority across commercial and open‑source LLM ecosystems (e.g., OpenAI, Anthropic, Google, Llama), including model selection, fine‑tuning, and hosting strategies.
• Proven ability to define enterprise‑wide GenAI standards , reference architectures, and reusable accelerators.
• Demonstrated leadership in establishing prompt engineering standards and orchestration patterns .
• Experience optimizing latency, 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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