AI / Machine Learning Engineer

AI/ML Engineer (3-5 yrs exp)

Salary: $100,000-$120,000

• Design and ship production pricing agents on NEO, owning them from prototype through production, as part of the firm's federated portfolio of production agents.
• Build retrieval that holds up in production: Graph RAG combining knowledge-graph traversal over products, rate cards, clients, and pricing policy with vector search, plus chunking, ranking, and grounding strategies that keep price explanations accurate and auditable.
• Design agent memory: episodic and semantic memory organized as memory nodes, with recall, summarization, and decay policies tuned per use case, so an agent remembers the history of a negotiation without leaking it across clients.
• Own organizational context management - assembling entitlement-, lineage-, and tenant-aware context so each agent reasons over only the pricing data it is allowed to see.
• Build and productionize ML model training pipelines for price optimization, elasticity, win-probability, and deal-scoring models using MLOps best practices, including feature engineering over billing, transaction, and deal history data on Databricks.
• Build batch and online inference for those models, meeting the latency a seller conversation requires while keeping every scored recommendation reproducible.
• Own model validation in a revenue-sensitive setting: back-testing against realized prices, challenger models, uplift and elasticity measurement, and monitoring for drift, margin erosion, and revenue leakage in production.
• Compose multi-agent workflows using A2A, and integrate tools and data through MCP servers (pricing engine, rate card store, CRM, billing and revenue data, Databricks, Snowflake, Kubernetes, Confluence, Bitbucket, Splunk).
• Build and run evals: task-level and end-to-end agent evaluations, regression suites, LLM-as-judge, numerical-correctness and explanation-faithfulness checks, and quality and safety gating before release.
• Produce the model documentation, test evidence, and controls the firm's model risk review requires for a model that influences client pricing, and keep that evidence current as models are retrained.
• Deploy and operate solutions on public cloud (AWS and/or Azure) with strong SDLC, security, resiliency, and observability practices.
• Partner with pricing, product, finance, and business partners across CIB and Payments to turn use cases into shipped, supported agents.

Required Qualifications, Capabilities, and Skills

• MS in Computer Science, Statistics, Mathematics, Machine Learning, or related field (or equivalent experience).
• Minimum 7 years of development experience, with at least 4 years working on AI/ML solutions.
• Hands-on experience building LLM-powered or agentic applications in production, including tracing, evaluations, and guardrails.
• Hands-on experience taking predictive or optimization models to production and keeping them healthy there, including validation, monitoring, and retraining.
• Practical RAG experience - retrieval quality, embeddings, and vector stores; Graph RAG a strong plus.
• Strong programming skills in Python, with deep knowledge of data structures, algorithms, machine learning, data mining, information retrieval, and statistics.
• Knowledge of Kubernetes (AWS EKS).
• Experience with training models in Databricks and SageMaker.
• Experience working with MLflow.
• Expert knowledge of at least one of: AWS, Azure, Kubernetes.
• Knowledge of data management and data model design; real-time processing using both SQL (e.g., Postgres) and NoSQL stores (e.g., OpenSearch, Redis).
• Excellent communication skills with the ability to partner effectively with senior technical and business stakeholders.
• Experience with pricing, revenue management, elasticity, or demand forecasting models in a production setting.
• Experience with mathematical optimization (linear and mixed-integer programming, e.g., OR-Tools or Gurobi) and with constrained recommendation problems.
• Experience with causal inference, uplift modelling, or experiment design where randomization is limited.
• Experience delivering models through model risk management or equivalent regulatory review.
• Experience working in the financial or payments domain at a large institution (Investment Banking, Markets, Securities Services, or adjacent), or with commercial card, treasury services, or billing and revenue systems.
• Experience with agent frameworks or runtimes, A2A, or MCP.
• Agent memory design (memory nodes, episodic/semantic memory) and organizational context management.
• Knowledge graphs and graph databases used for retrieval.
• Understanding of LLM fine-tuning and small language model inference.
• Ability to develop full-stack products using modern JavaScript/TypeScript frameworks (e.g., Next.js, Svelte) for agent UIs (AG-UI / NEO UI SDK).
• Knowledge of high-performance languages such as Go or Rust.