Applied Machine Learning Scientist - Vice President

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 where appropriate) 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 multiple partner teams, such as Business, Technology, Product Management, Design, Analytics, and Model Governance to deploy solutions into production.

Build domain understanding to identify high-impact opportunities, ensure responsible AI usage, and drive measurable outcomes (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, Capabilities, And Skills

• MS with 7+ years, or PhD with 4+ years of hand-on industry experience in building and deploying machine learning systems (NLP/Information Retrieval/Recommendation System and/or GenAI) in production environment
• Good understanding of the latest advancement of NLP concepts, such as the 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 with 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, Capabilities, And Skills

• 5 years of hands-on experience with virtual assistant model development and optimization
• Orchestrating multi-agent teams with supervisor agents, debate/consensus mechanisms, and role-specialized toolkits for complex enterprise tasks.
• Building agent governance and eval 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.