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About This Role
Overview
The Head of Forward Deployed Engineering (AI) leads a team of Forward Deployed Engineers responsible for partnering directly with business units to design, build, and deliver AI-powered solutions that address real-world business challenges. This leader oversees concurrent engagements across multiple business areas, ensuring solutions are delivered with speed, quality, security, and alignment to enterprise AI platform standards.
Serving as the primary connection between business stakeholders and the central AI Engineering organization, this role establishes engagement models, drives execution excellence, and ensures reusable, governed, and scalable AI capabilities are effectively deployed across the enterprise. The position is responsible for strategic team leadership, staffing, budget management, technical oversight, stakeholder engagement, and the ongoing development of engineering talent.
Primary Responsibilities
- Lead, mentor, and develop a team of Forward Deployed Engineers, establishing priorities across multiple concurrent business engagements and resolving resource allocation challenges.
- Serve as the technical and delivery owner for Forward Deployed Engineering engagements by partnering with business stakeholders to identify opportunities, scope AI use cases, define solution architectures, and ensure successful delivery.
- Manage and participate in consultations with business and technology leaders to analyze short- and long-term business requirements and recommend innovative AI-driven solutions that anticipate future needs.
- Establish and maintain strong partnerships with business units, technology teams, and executive stakeholders while acting as the primary escalation point for engagement-related issues.
- Ensure AI solutions developed within business units align with enterprise AI platform standards, governance requirements, architecture principles, and long-term technology strategies.
- Drive adoption and reuse of centralized AI services, platforms, tooling, and shared engineering components to avoid fragmented or one-off implementations.
- Serve as the voice of the field by identifying recurring business needs, platform gaps, tooling opportunities, and reusable capabilities that should be developed centrally.
- Define and continuously improve engagement standards, including intake processes, solution discovery, project scoping, delivery methodology, deployment practices, and post-launch support models.
- Establish and enforce engineering quality standards including code quality, testing, security, data protection, model governance, production readiness, and operational support requirements.
- Oversee multiple AI solution development initiatives and ensure timely delivery of projects across diverse business functions and stakeholder groups.
- Monitor industry trends, emerging technologies, AI advancements, and vendor capabilities to identify opportunities that improve business outcomes and engineering effectiveness.
- Research, evaluate, and recommend technologies, products, vendors, and engineering approaches that support the organization's AI strategy.
- Develop and manage departmental budgets, resource plans, vendor relationships, and staffing strategies to support organizational objectives.
- Provide regular reporting on engagement performance, delivery status, operational metrics, business impact, resource utilization, and strategic priorities to senior leadership.
- Exercise managerial authority regarding staffing, hiring, coaching, performance management, promotions, compensation recommendations, succession planning, and employee development.
- Foster an environment that promotes collaboration, innovation, continuous learning, belonging, and reflects the M&T Bank brand.
- Understand and adhere to the Company's risk and regulatory standards, policies, and controls in accordance with the Company's Risk Appetite. Design, implement, maintain, and enhance internal controls to mitigate risk on an ongoing basis. Identify risk-related issues needing escalation to management.
- Maintain M&T internal control standards, including timely implementation of internal and external audit findings together with any issues raised by external regulators, as applicable.
- Complete other related duties as assigned.
Scope of Responsibilities
Leads a team of Forward Deployed Engineers responsible for delivering AI solutions directly within business units while ensuring alignment with enterprise architecture, AI governance, engineering best practices, and platform strategy. Oversees multiple concurrent AI engagements spanning software engineering, machine learning, generative AI, solution architecture, and stakeholder delivery functions.
Acts as a key member of the AI Engineering leadership team, balancing business-specific delivery objectives with broader enterprise platform consistency, scalability, risk management, and technology strategy.
Supervisory/Managerial Responsibilities
Typically manages 10 to 20 employees, including senior engineers, technical leads, and other engineering professionals.
Education and Experience Required
- A combined minimum of 11 years' higher education and/or work experience, including a minimum of 4 years' engineering and/or architecture experience and 5 years' leadership experience including people management.
- Experience delivering software, data, machine learning, or AI solutions in close partnership with business and product stakeholders.
- Hands-on experience building and deploying production AI applications utilizing large language models (LLMs), retrieval-augmented generation (RAG), AI agents, model APIs, machine learning models, or related technologies.
- Experience managing multiple concurrent initiatives with competing stakeholder priorities and delivery timelines.
- Strong understanding of solution architecture, software engineering practices, application development methodologies, and production support models.
- Demonstrated ability to establish engineering standards and perform effective architecture, code, and design reviews.
- Proficiency with project management, collaboration, reporting, and productivity tools.
- Capable of leading large, complex technical initiatives across multiple teams and business areas.
- Excellent analytical, problem-solving, organizational, and decision-making skills.
- Excellent verbal and written communication skills, including experience presenting technical concepts to senior executives and non-technical audiences.
- Experience fostering collaboration, teamwork, and employee development while leading geographically distributed teams.
- Understanding of technical, operational, regulatory, and business impacts associated with AI-enabled solutions.
Education and Experience Preferred
- Bachelor's degree in Computer Science, Engineering, Data Science, Information Technology, or a related field.
- Experience leading Forward Deployed Engineering, Solutions Engineering, Professional Services Engineering, Customer Engineering, or similar field-facing engineering organizations.
- Experience partnering with centralized platform, architecture, governance, or engineering enablement functions.
- Experience building, scaling, or transforming a Forward Deployed Engineering organization.
- Experience delivering AI solutions within highly regulated industries such as financial services, healthcare, insurance, or similar environments.
- Strong knowledge of enterprise AI governance, model governance, use case intake processes, risk assessment frameworks, and responsible AI practices.
- Extensive experience with AI/ML platforms, generative AI technologies, cloud-native architectures, and production engineering practices.
- Demonstrated success developing engineering talent and building high-performing technical teams.
- Strong understanding of enterprise technology strategy, platform operating models, and organizational change management.
- Experience influencing senior stakeholders and driving strategic technology initiatives across large organizations.
Skills Required
- People leadership, coaching, hiring, and organizational development
- Applied AI and machine learning solution architecture
- Generative AI technologies including LLMs, RAG, agents, and model integrations
- Stakeholder management and executive communication
- Technical delivery leadership and program execution
- Resource planning and prioritization across concurrent engagements
- Software engineering best practices including testing, code review, CI/CD, and security fundamentals
- Enterprise architecture and platform alignment
- AI governance, risk management, and compliance awareness
- Strategic planning, budget management, and organizational leadership
- Ability to translate business challenges into scalable AI solutions
- Ability to translate field engagement patterns into platform capabilities and enterprise standards
M&T Bank is committed to fair, competitive, and market-informed pay for our employees. The pay range for this position is $139,700.00 - $232,900.00 Annual (USD). The successful candidate’s particular combination of knowledge, skills, and experience will inform their specific compensation. Location
Buffalo, New York, United States of America
Salary Context
This $139K-$232K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At M&T 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($186K) sits 12% below the category median. Disclosed range: $139K to $232K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
M&T Bank AI Hiring
M&T Bank has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Remote, US, Buffalo, NY, US. Compensation range: $194K - $247K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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