The Lead AI/ML Engineer owns the brain of the Navanta AI platform - retrieval, text-to-metrics, model serving, tool orchestration, and the evaluation harness that keeps answers honest. Working under the SVP of Technology and Commercial AI and in close collaboration with the data, platform, and product teams, this role makes "correct and verifiable" the product's default - the foundation of trust in a regulated banking environment where a confident wrong number loses the account.
Key Responsibilities
• Build Navanta's retrieval and verifications over data systems, with shown queries and citations for every answer
• Stand up self-hosted open-weight models serving and embeddings inside each bank's environment or shared environments for Navanta; evolve RAG to a dedicated standard
• Design the MCP tool layer that exposes a small, audited set of read-only tools (metrics, documents, customer 360), eventually growing into read/write tools with heavy amounts of regulated, highly sensitive data
• Build and maintain the evaluation harness - golden-question regression, groundedness and retrieval metrics, explicit "I don't know" behavior - and make it a release gate
• Implement LLM guardrails: PII redaction in prompts and context, prompt-injection defenses, and cost and row limits aligned to regulatory security expectations
• Partner with data teams so the model selects governed metrics from the semantic layer rather than improvising SQL
• Document model architecture, evaluation methodology, and guardrail controls to support customer security reviews and audit readiness
• Track latency, cost, and quality trade-offs across model versions and deployment configurations
Core Competencies
• Accuracy and evaluation orientation - a demonstrated focus on verifiability and groundedness, not just compelling demos
• Production LLM/RAG engineering: retrieval pipelines, tool orchestration, prompt engineering, and guardrail implementation
• Security and compliance mindset: PII handling, prompt-injection defense, and least-privilege tool access aligned to NIST CSF 2.0 principles
• Cross-functional collaboration with data and platform engineering to deliver a governed, auditable AI system
Key Performance Indicators (KPIs)
• Golden-question accuracy - maintained or improved release over release against the verified question set
• Groundedness rate: percentage of assistant answers fully supported by retrieved context
• PII redaction coverage and zero prompt-injection incidents in production
• Model serving latency and cost per query within defined targets
• Evaluation harness adoption as a release gate - zero releases without passing the regression suite
Qualifications
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required.
• 6-10+ years building software, with 2-3+ years shipping production LLM, RAG, or NLP systems used by real people - not prototypes
• A demonstrated focus on accuracy and evaluation, not just demos
• Strong Python and solid software-engineering fundamentals
• Comfort operating self-hosted open-weight models and reasoning about latency, cost, and quality trade-offs
Core Technologies
• Languages: Python
• Serving & inference: vLLM, Ollama; GPU / CUDA familiarity, NVIDIA Enterprise (NVAIE)
• RAG & retrieval: LlamaIndex or Haystack; Qdrant, pgvector; embeddings
• Orchestration: MCP, tool / function calling
• Structured querying: text-to-SQL; semantic layers (Cube / dbt MetricFlow)
• Evaluation & guardrails: groundedness and eval frameworks, PII redaction, prompt-injection defense
Nice to Have
• Experience in regulated or high-stakes domains where a wrong answer is costly
• Fine-tuning, adapters, and retrieval-quality optimization
• Familiarity with banking and finance terminology
Education and/or Experience
• Bachelor's degree in computer science, mathematics, or a related technical field, or equivalent hands-on experience
• Experience in the financial services industry or a regulated, high-accuracy AI application environment strongly preferred
Work Structure & Expectations
• Full-time role combining ongoing model operations and evaluation with initiative-based build-out of the data retrieval, guardrail, and serving infrastructure
• Close collaboration with data engineering, platform engineering, and product teams; on-call rotation covering reliability in production
Physical Demands
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this job, the employee is regularly required to sit and use hands to finger, handle, or touch objects, tools, or controls. The employee frequently is required to talk or hear. The employee is occasionally required to stand; walk; and stoop, kneel, crouch, or crawl. The employee must occasionally lift and/or move up to 10 pounds, usually waist high, up to 50 feet away. Specific vision abilities required by this job include close vision and the ability to adjust focus.
Work Environment
The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
• Typical office environment
• Up to 20% travel time may be required
