Job description

Description: This role requires working onsite 4 days per week, and a F2F interview at the client’s Jersey City location is mandatory. Only USCs/GCs are eligible POSITION OVERVIEW : Lead Applied AI Engineer POSITION GENERAL DUTIES AND TASKS : Lead Applied AI Engineer Role purpose The Lead Applied AI Engineer designs, builds, deploys, and continuously improves secure, scalable, and supportable AI solutions on MUFG AIRP. The role applies foundation models, LLMs, retrieval-augmented generation (RAG), and agentic AI patterns to high-value business problems in a regulated financial-services environment. This is a senior hands-on technical leadership role. The incumbent turns AI concepts and prototypes into production capabilities, applies reusable engineering patterns, and partners across product, architecture, cloud/platform, security, risk, compliance, data, and operations. Primary ownership Lead end-to-end delivery of production LLM applications, RAG pipelines, AI services, and agentic workflows for MUFG AIRP use cases. Own the Applied AI lifecycle: discovery, design, experimentation, evaluation, deployment, monitoring, incident response, rollback, and continuous improvement. Applies reusable patterns for model access, retrieval, tool integration, APIs, evaluation, observability, security, and operational readiness. Make evidence-based decisions on model selection, prompts and context, retrieval, model adaptation, inference optimization, cost, and build-versus-buy options. Provide technical leadership through architecture reviews, engineering standards, mentoring, and cross-functional delivery coordination. Key responsibilities Lead the design, build, deployment, and continuous improvement of production LLM, RAG, and agentic AI solutions for priority MUFG business workflows. Translate business needs into secure, scalable, and supportable AI solution designs with clear success measures, use boundaries, human-oversight requirements, and operational ownership. Design and optimize application-specific RAG implementations using approved AIRP capabilities, including document onboarding, metadata, chunking, retrieval configuration, reranking, grounding, citations, and source traceability. Design controlled, business-specific agentic workflows using approved orchestration and secure tool-execution patterns, including validation, least-privilege access, error handling, and human approval where required. Select and apply appropriate models, prompts, context strategies, retrieval approaches, and integration patterns based on quality, risk, latency, resilience, cost, and business requirements. Define and execute evaluation and release-readiness practices for task quality, groundedness, retrieval relevance, safety, reliability, latency, and cost. Build and operate application services, APIs, and enterprise integrations using AIRP-approved model access, cloud, container, CI/CD, security, and observability capabilities. Improve application performance through prompt and context optimization, model routing, caching, batching, model adaptation, fine-tuning, quantization, or inference optimization where appropriate. Own the application-level AI lifecycle for assigned solutions, including versioning, evaluation gates, controlled releases, monitoring, incident response, rollback, and continuous improvement. Embed security, privacy, Responsible AI, model-risk, data-protection, and audit requirements into each solution, partnering with relevant control functions as needed. Apply AIRP reference architectures and engineering standards; contribute reusable application patterns, implementation feedback, and platform enhancement requirements to the AIRP roadmap. Provide technical leadership for assigned initiatives through design reviews, mentoring, documentation, and collaboration with product, architecture, platform, cybersecurity, data, risk, compliance, and operations teams. Required qualifications 7+ years in AI/ML engineering, Applied AI, software

Requirements

Must-haves

This role requires working onsite 4 days per week, and a F2F interview at the client’s Jersey City location is mandatory.
Only USCs/GCs are eligible
POSITION OVERVIEW : Lead Applied AI Engineer POSITION GENERAL DUTIES AND TASKS : Lead Applied AI Engineer
Role purpose
Primary ownership
Lead end-to-end delivery of production LLM applications, RAG pipelines, AI services, and agentic workflows for MUFG AIRP use cases.
Own the Applied AI lifecycle: discovery, design, experimentation, evaluation, deployment, monitoring, incident response, rollback, and continuous improvement.
Applies reusable patterns for model access, retrieval, tool integration, APIs, evaluation, observability, security, and operational readiness.
Make evidence-based decisions on model selection, prompts and context, retrieval, model adaptation, inference optimization, cost, and build-versus-buy options.
Provide technical leadership through architecture reviews, engineering standards, mentoring, and cross-functional delivery coordination.
Lead the design, build, deployment, and continuous improvement of production LLM, RAG, and agentic AI solutions for priority MUFG business workflows.
Translate business needs into secure, scalable, and supportable AI solution designs with clear success measures, use boundaries, human-oversight requirements, and operational ownership.
Design controlled, business-specific agentic workflows using approved orchestration and secure tool-execution patterns, including validation, least-privilege access, error handling, and human approval where required.
Select and apply appropriate models, prompts, context strategies, retrieval approaches, and integration patterns based on quality, risk, latency, resilience, cost, and business requirements.
Define and execute evaluation and release-readiness practices for task quality, groundedness, retrieval relevance, safety, reliability, latency, and cost.
Build and operate application services, APIs, and enterprise integrations using AIRP-approved model access, cloud, container, CI/CD, security, and observability capabilities.
Improve application performance through prompt and context optimization, model routing, caching, batching, model adaptation, fine-tuning, quantization, or inference optimization where appropriate.
Own the application-level AI lifecycle for assigned solutions, including versioning, evaluation gates, controlled releases, monitoring, incident response, rollback, and continuous improvement.
Embed security, privacy, Responsible AI, model-risk, data-protection, and audit requirements into each solution, partnering with relevant control functions as needed.
Apply AIRP reference architectures and engineering standards; contribute reusable application patterns, implementation feedback, and platform enhancement requirements to the AIRP roadmap.
7+ years in AI/ML engineering, Applied AI, software

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

aillmci/cdml

Platform: jobdiva
Effort: medium