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
Job Family Group
Technology
Job Family
Applications Development
Time Type
Full time
Primary Location Full Time Salary Range
$145,100.00 - $217,700.00
Most Relevant Skills
Please see the requirements listed above.
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.
Automated Processing and AI
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This job opening is for an existing job vacancy.
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