Vice President, Applied AI & GenAI Systems

Applied AI Machine Learning Lead Lead the development of scalable, production-grade advanced machine learning solutions across natural language processing, speech recognition, recommendation systems, information retrieval, and agentic AI. Design and productionize large language model powered systems such as Retrieval Augmented Generation (RAG), tool/function‑calling agents, and structured generation to automate complex workflows and enhance customer experiences. Job Responsibilities Lead and deploy state-of-the-art advanced machine learning systems across NLP, speech recognition, recommendation systems, and information retrieval. Design and build agentic AI systems for multi‑step workflows, including tool/function calling, multi‑agent orchestration, planning, grounding, and safety guardrails. Use reinforcement learning (policy optimization, bandits, RLHF-style approaches) to improve personalization, dialog policies, and sequential decision‑making systems. Fine‑tune and adapt LLMs/SLMs using PEFT (LoRA, AdaLoRA, IA3), distillation, and quantization; optimize for quality, latency, cost, and production constraints. Select and innovate on ML strategies for various banking problems. Analyze and evaluate the ongoing performance of developed ML systems. Collaborate with Business, Technology, Product Management, Design, Analytics, and Model Governance teams to deploy solutions into production. Build domain understanding to identify high‑impact opportunities, ensure responsible AI usage, and drive measurable outcomes such as customer experience, automation, accuracy, and efficiency. Implement privacy, safety, and security controls for GenAI systems, including PCI handling/redaction, policy checks, jailbreak resistance, and auditability. Required Qualifications MS with 7+ years or PhD with 4+ years of hands‑on industry experience building and deploying machine learning systems (NLP, Information Retrieval, Recommendation System, and/or GenAI) in production environments. Good understanding of the latest advances in NLP concepts such as transformer architecture, knowledge distillation, transfer learning, and representation learning. Applied GenAI experience with LLMs and the ability to fine‑tune and deploy SLMs for targeted use cases, familiarity with prompt design, grounded generation, and RAG. Experience scaling LLM systems (caching, batching, prompt/version governance, evaluation harnesses). Strong foundation in machine learning, deep learning, and statistical modelling, including model evaluation and error analysis. Solid understanding of Information Retrieval concepts (indexing, ranking, dense/sparse retrieval, re‑ranking) and/or recommendation systems. Ability to design experiments—establish strong baselines, choose meaningful metrics, and evaluate model performance rigorously. Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments. Proficiency in Python and common ML libraries (PyTorch/TensorFlow, Hugging Face, scikit‑learn), and ability to write production-quality code. Ability to collaborate in cross‑functional environments with product, engineering, and control partners. Solid written and spoken communication skills. Preferred Qualifications 5 years of hands‑on experience with virtual assistant model development and optimization. Experience orchestrating multi‑agent teams with supervisor agents, debate/consensus mechanisms, and role‑specialized toolkits for complex enterprise tasks. Building agent governance and evaluation suites: red‑teaming, adversarial tests, safety scorecards, regression suites for prompts/tools. Experience with RL/bandits, preference optimization, or human feedback loops for personalization. Experience in regulated finance domains and working with risk/control processes. Experience with MLOps/LLMOps: CI/CD for models, monitoring/alerting, model versioning, evaluation of pipelines, and rollback strategies. Experience with A/B experimentation and data/metric-driven product development. Equal Opportunity Employer We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy, or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation. #J-18808-Ljbffr Califesciences