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About This Role
Purpose of Position:
The AI Engineer is responsible for developing, testing, deploying, and supporting enterprise AI solutions that advance the Bank's Data & AI strategy. Reporting to the Head of Data & AI and working closely with the Lead AI Engineer, this role contributes to the design and implementation of AI agents, large language model (LLM) applications, retrieval solutions, integrations, and governance controls using Databricks and approved enterprise AI platforms.
This position provides an opportunity to work across data engineering, software development, AI governance, and business process optimization while developing expertise in generative AI, agentic architectures, and responsible AI practices within a regulated financial services environment.
Essential Job Functions:
AI Solution Development:
- Design, develop, test, and support AI-powered applications, copilots, and agent-based solutions using Python, Databricks, and approved enterprise AI platforms.
- Assist in building retrieval-augmented generation (RAG) solutions, workflow automations, and multi-agent architectures to support business objectives.
- Develop and maintain integrations between AI solutions and enterprise systems, databases, APIs, and business applications.
- Participate in prompt engineering, context management, tool integration, and agent orchestration activities.
Model Evaluation, Monitoring, and Optimization:
- Support the creation and maintenance of evaluation frameworks, test datasets, and benchmarking methodologies to measure AI performance.
- Assist in monitoring model quality, accuracy, safety, latency, and cost efficiency through dashboards and operational metrics.
- Troubleshoot AI application issues and contribute to performance tuning and continuous improvement initiatives.
- Participate in validation activities to ensure AI solutions meet business, regulatory, and governance requirements.
Data Engineering and Knowledge Management:
- Prepare, transform, and validate structured and unstructured data for AI and analytical applications.
- Collaborate with Data Engineering and Data Governance teams to ensure AI solutions utilize trusted, certified, and well-governed data sources.
- Support the development and maintenance of vectorized knowledge repositories and retrieval services.
- Contribute to data lineage, metadata, and documentation efforts supporting audit and governance requirements.
Governance, Security, and Compliance:
- Adhere to established standards for Responsible AI, information security, privacy, model governance, and change management.
- Support the documentation and evidence collection required for model reviews, audits, and regulatory examinations.
- Assist with monitoring guardrails related to AI safety, accuracy, traceability, observability, and operational effectiveness.
- Follow enterprise policies governing access controls, data handling, and model lifecycle management.
Collaboration and Continuous Learning:
- Partner with data engineers, BI engineers, analysts, and business stakeholders to deliver practical AI solutions.
- Support the development of dashboards and operational reporting that measure AI adoption, effectiveness, and business value.
- Research emerging AI technologies, tools, and engineering practices and share findings with the Data & AI team.
- Participate in peer reviews, technical discussions, and knowledge-sharing activities to support team growth and innovation.
- Perform other duties as directed.
Knowledge, Skills and Experience Requirements:
- Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Information Systems, or a related field.
- 1-3 years of professional experience in software engineering, data engineering, machine learning, artificial intelligence, or a related technical discipline.
- Proficiency in Python and SQL.
- Experience working with cloud-based data platforms, preferably Databricks or similar technologies.
- Foundational knowledge of large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG), and AI application development.
- Strong analytical, problem-solving, and troubleshooting abilities.
- Effective verbal and written communication skills with the ability to document technical solutions clearly.
Preferred Qualifications:
- Experience with Databricks, Azure, Git, CI/CD pipelines, and API development.
- Familiarity with agentic AI frameworks such as LangGraph, LlamaIndex, Semantic Kernel, or comparable technologies.
- Exposure to AI governance, model evaluation methodologies, and responsible AI practices.
- Experience supporting dashboards, reporting solutions, or operational monitoring capabilities.
- Understanding of AI and Data Governance principles.
- Prior experience within financial services, banking, or another regulated industry.
Our job titles may span more than one career level. The starting base salary for this role is between $120,000.00 – $145,000.00. The actual base pay is dependent upon many factors, such as: training, transferrable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future.
Amalgamated Bank is an Equal Opportunity and Affirmative Action Employer, Minorities / Females / Individuals with Disability / Veterans. AmeriCorps, Peace Corps and other national service alumni are encouraged to apply. View our Pay Transparency Statement. Submission of a resume or any information regarding your qualifications does not constitute a promise or offer of employment. At Amalgamated Bank, we consider an applicant to be someone who has interviewed at least once, in person, with the hiring manager. Amalgamated Bank does not sponsor applicants for work visas.
Hybrid Work Model
Effective February 18, 2025, employees in office-based positions will be working a Hybrid work schedule consisting of three days or more, on-site per week, Monday - Thursday, although the specific days may vary by site or organization, with Friday designated as a remote-working day, unless business critical tasks require an on-site presence. This Hybrid work model does not apply to, and daily in-person attendance is required for, the contact center, branch service roles, and general services where the work to be performed is located at a Company site; positions covered by a collective-bargaining agreement (unless the agreement provides for hybrid work); or any other position for which the Company has determined the job requirements cannot be reasonably met working remotely. Please note, this Hybrid work model guidance does not apply to roles that have been designated as “remote”. Search Firm Representatives- Please Read Carefully
Amalgamated Bank does not accept unsolicited assistance from search firms for employment opportunities. All CVs / resumes submitted by search firms to any employee at our company without a valid written search agreement in place for the position will be deemed the sole property of our company. No fee will be paid in the event a candidate is hired by our company as a result of an agency referral where no pre-existing agreement is in place. Where agency agreements are in place, introductions are position specific. Please, no phone calls or emails.
Salary Context
This $120K-$145K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Amalgamated Bank, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($132K) sits 38% below the category median. Disclosed range: $120K to $145K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Amalgamated Bank AI Hiring
Amalgamated Bank has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $145K - $200K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 tracked positions. That's 4% above the national median.
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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