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About This Role
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
Who is Navanta?
Navanta is the trusted technology and services partner for community financial institutions, unifying critical systems, security, cloud infrastructure, and support into one seamless, purpose built experience. With more than 35 years of banking expertise — from Managed IT to Core Banking, CRM, and Advisory Services — Navanta helps institutions simplify complexity, reduce risk, and strengthen daily operations. Navanta empowers community bankers and their people to thrive together. Go Bankers, Go.™
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Navanta, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Navanta AI Hiring
Navanta has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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