Applied AIML Lead-Platform AI Acceleration

Role overview

Responsibilities

• Design, develop, and deploy ML, LLM, and generative AI solutions from concept through production, owning reliability and operability.
• Work with product managers, data scientists, ML engineers, and stakeholders to understand requirements and prioritize use cases.
• Build secure, testable services and libraries integrating LLMs, tool use, RAG, and agentic workflows.
• Build end-to-end RAG and Agentic RAG pipelines, including chunking, indexing, retrieval tuning, reranking, and grounding checks.
• Fine-tune generative models for NLP tasks such as summarization and text generation.
• Mentor junior engineers through design reviews, code reviews, pairing, and coaching.
• Monitor solution performance to support reliability and compliance; communicate capabilities and results to technical and non-technical audiences.
• Apply new AI/ML techniques and external APIs; collaborate with Infrastructure Platforms AI teams to promote scalable, resilient, secure AI adoption.

Required qualifications

• Proven delivery of LLM-enabled applications using agentic patterns, including tool use, orchestration, guardrails, and structured outputs.
• Hands-on experience building and reliably operating MCP integrations in production.
• Production software or systems engineering experience delivering services in secure, regulated environments.
• Expert Python engineering, including production-grade design, testing, debugging, and performance tuning.
• Advanced prompt engineering, including system prompts, few-shot prompting, tool or function calling, and schema-constrained outputs such as JSON Schema.
• Understanding of agentic AI system layers, including context management, harness design, and loop engineering.

AI/ML and engineering experience

• Experience building conversational AI with RAG, Agentic RAG, and Graph RAG; proficiency in modern RAG and multi-agent system architectures.
• Experience scaling AI/ML workloads with distributed training or serving frameworks such as Ray and GPU acceleration such as CUDA.
• Familiarity with LLM quality, safety, and reliability evaluation, guardrails, content filtering, and Responsible AI practices.
• Proficiency in secure GenAI engineering, including data sensitivity, secure handling of inputs and outputs, and resilience and security requirements.
• Demonstrated success driving adoption of enterprise-approved AI-assisted coding, review, testing, or troubleshooting tools.

Preferred qualifications

• Financial services industry experience.
• Understanding of FinOps for LLMs.
• Java programming experience is a plus.