Job responsibilities

• Develop 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

• BS with 5+ years, or MS with 3+ 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

• 2 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 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.

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.