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About This Role
Overview:
The Director of Enterprise Automation, AI & Analytics is responsible for leading enterprise-wide transformation initiatives by identifying, designing, and implementing process improvements that maximize the use of artificial intelligence (AI), robotic process automation (RPA), advanced workflow tools, SharePoint Online, and data and analytics capabilities. This role develops and executes the strategy and roadmap for the functions under the Director's leadership, aligning with the Bank's strategic objectives and overall technology direction to drive operational efficiency, strengthen customer experiences, support data-informed decision making, and advance innovation across the organization.
Core responsibilities include developing and deploying intelligent automation solutions, orchestrating end-to-end electronic workflows, implementing AI-enabled business intelligence and analytics, supporting data management and governance within the functions under this role, optimizing document governance, and modernizing collaboration platforms. The position also provides strategic oversight for process design, automation, application and analytics solutions, enhanced user experiences, and regulatory and compliance alignment within technology-enabled solutions.
The Director provides leadership and direction through subordinate managers for the Business Process Management, Business Automation Development, and Enterprise Data and Analytics functions. The role establishes departmental objectives, priorities, and multi-year roadmaps; leads the development and delivery of process, automation, application, data, and analytics solutions; and maintains a prioritized portfolio of initiatives based on strategic alignment, business value, operational impact, and risk.
The Director leads the responsible adoption and effective use of AI and Generative AI within the functions under this role. This includes identifying and prioritizing opportunities to apply AI, incorporating AI and intelligent automation into process design and solution delivery, establishing appropriate AI adoption objectives for the teams, and partnering with Risk Management, Compliance, Information Security, Legal, Internal Audit, and other stakeholders as appropriate to ensure AI-enabled solutions meet applicable regulatory, risk, security, and governance requirements.
The Director collaborates with business leadership, IT teams, and vendor partners to identify opportunities and deliver integrated process, automation, workflow, application, data, and analytics capabilities that reduce manual effort, increase employee productivity, improve decision making, and strengthen organizational knowledge sharing. This role actively champions the effective use of BPM, SharePoint Online, automation, AI, business intelligence, and data technologies, functioning as a solutions consultant who applies agile development practices, process re-engineering, and emerging AI-driven methods.
The Director provides strategic and operational leadership for the delivery of information and analytics solutions, including data preparation, reporting and analytics, data exploration, business intelligence, and information delivery capabilities. The role supports data governance, data quality, metadata, and governed self-service analytics within the functions under its leadership and ensures that data supporting automation, analytics, and AI-enabled solutions is appropriately managed and fit for purpose.
Within the scope of authority and expertise, the Director ensures that internal processes and supporting technology capabilities are continuously evaluated and optimized through process re-engineering, automation, AI, workflow integration, and effective use of data, delivering high levels of service, responsiveness, efficiency, and innovation to stakeholders across the enterprise. Responsibilities:
- Provide leadership and direction for the Business Process Management, Business Automation Development, and Enterprise Data and Analytics teams through three subordinate managers. Set departmental strategy, objectives, and multi-year roadmaps aligned to the Bank’s strategic plan and technology architecture.
- Understands business strategic objectives to ensure technology and data architectural vision is fully aligned with those objectives and strategies. Anticipates future business challenges and brings innovative technology solutions and options to leadership.
- Lead the redesign of processes to integrate AI tools such as intelligent automation (RPA + AI), machine learning, Generative AI, and natural language processing. Engage in discovery and identification of automation processes; proactively seek and suggest improvements; and outline any risks and recommend optimal solutions.
- Oversee the overall delivery of Robotic Process Automation, workflow, and AI-enabled automation capabilities as it aligns with the Banks business strategies and roadmap. Identify business outcomes that need to be achieved through automation.
- Define, promote and utilize best practices for automation and AI capabilities. Serve as a subject matter expert (SME) for others seeking to utilize RPA and AI technologies to automate and streamline existing manual business processes.
- Lead, through subordinate managers, teams of business process analysts, automation and integration developers and data and analytics professionals. . Develop and lead a cross functional Center of Excellence driving prioritization, evolution, and general execution of these services.
- Lead and manage requirement definition sessions with business units to create new strategic solutions. Leads efforts to identify opportunities for task automation, AI enablement, process improvements, and enhancements that result in increased quality, efficiencies, and return on investment (ROI).
- Oversee the business process management, automation, application, and data platform components life cycle and participate in the design, coding, testing, implementing and support of application software and enterprise integration. Lead implementation of software upgrades by analyzing software release documentation to identify and communicate bug fixes and enhancements.
- Define and report AI and automation value metrics, including hours returned, cycle time reduction, error and rework reduction, adoption rates, and return on investment, to executive leadership and the Board as requested.
- Lead team by establishing and executing a vision for the delivery of information and analytics solutions to the organizations key stakeholders. Responsibilities include enterprise data strategy, data architecture, data warehouse and business intelligence delivery, data preparation, reporting analytics, data exploration, and information delivery portals.
- Manage the activities of department staff. Conduct performance reviews and makes hiring, salary and promotion recommendations. Provide for the ongoing direction, coaching, and professional development of managers and staff in developing and implementing plans and objectives.
- Provide strategic and analytic support focused on the evolution of the business process management, automation, and enterprise data technology platforms to meet the Bank’s future requirements. Ensure that the team engages in continuous improvement, as well as exploring and improving on subsequent deployments through retrospective analyses.
- Act as a change agent leading internal focus groups to address end-user issues, usability, and enhancements to leverage BPM, automation, AI, and analytics applications. Schedule and facilitate ongoing business and technical roadmap sessions to ensure leveraging of all BPM application features and functionality.
- Develop and maintain a single prioritized portfolio of process, automation, application, and data initiatives, allocating resources based on strategic alignment, business value, operational impact, and risk.
- Monitor and manage overall system risk of business process management, automation, AI, and data technology hardware and software to include adhering to FFIEC guidelines, annual risk assessments, model risk management, vendor risk, and internal and external audit reviews.
- Communicate with and coordinate efforts of various consultants and vendors. Assist with vendor management, including periodic meetings, issue escalation, invoicing, reporting and tracking of efforts.
- Provide input to the budgeting process recommending capital purchases, software investments and staffing requirements.
- Responsible for coordinating, testing and documenting the Information Technology Disaster Recovery Plan for the business process management, automation, application, and data warehouse and business intelligence platforms.
- Ensure team adherence to data governance policies and encourage a data stewardship mindset within the team, promoting accountability for data quality and compliance. Oversee data quality metrics and remediation efforts relevant to your department’s data.
- Ensures the integrity and confidentiality of data residing in the Bank’s information systems.
- Performs all duties in accordance with prescribed regulatory compliance guidelines and in conformance with established Bank policies, procedures and objectives.
- Refer sales opportunities to other departments or bank affiliates in support of established objectives.
- Performs related and unrelated duties as may be required.
Requirements:
Education* Bachelor's Degree in computer science, information systems, business, analytics or a related discipline is required
- Master's Degree is preferred
Work Experience* 10+ years system, operational and business experience delivering enterprise technology, automation and data solutions is
- 5+ years technical management experience required, including experience leading through subordinate managers and leading multi-disciplinary teams across business analysis, application and automation development, and data and analytics is
- Demonstrated experience defining and executing an enterprise automation, digital transformation, or data and analytics strategy with measurable business outcomes is
- Experience within banking or another highly regulated industry is strongly preferred is
Additional Requirements* Working knowledge of the following Microsoft technologies and development tools; SharePoint Online, Microsoft 365, Microsoft Teams, Microsoft Copilot, Power Platform, SQL Server.NET/C# and other components of Microsoft technology.
- Working knowledge of enterprise automation and workflow platforms such as UiPath, Nintex/K2 and Microsoft Power Platform.
- Demonstrated experience evaluating, governing and scaling AI, Generative AI, machine learning and RPA capabilities in a regulated environment, including AI and model risk management, responsible AI controls, and third party AI due diligence.
- Working knowledge of data warehousing, ETL, semantic modeling, business intelligence and visualization (SQL Server, SSIS, Power BI), cloud data platforms (Microsoft Azure, Microsoft Fabric, Snowflake or comparable), and data governance frameworks such as DAMA-DMBOK or DCAM.
- Strong understanding of banking operations, FFIEC guidance, GLBA, audit and examination expectations, information security, vendor risk and model risk management.
- Solid communication skills in articulating strategies and plans to team members and Senior Management (as necessary) as well as the ability to work effectively with internal team members, business line owners, auditors, examiners and vendors.
- Strong analytical, interpersonal and problem solving abilities to identify and prioritize critical path issues and risks and recommend mitigation strategies.
- Ability to consistently show initiative and resourcefulness to solve problems and ensure that high-quality work is produced, while respecting deadlines and budgetary considerations.
- Excellent analytical skills as proven through past success creating, implementing, and executing transformational strategy
- Demonstrated ability to lead organizational change and to develop managers and technical staff.
- Must have schedule flexibility and be able to work extended hours and weekends.
Licenses and Certifications* Must have a valid driver’s license. Regular travel to bank sites required
- Project Management Professional (PMP), Certified Business Process Professional (CBPP), Lean Six Sigma, Certified Data Management Professional (CDMP), or a comparable AI or data governance certification desired.
Expected Pay Range: From: $137,316To: $250,608
The pay range is the salary we in good faith expect to pay for this role at the time of posting. Actual compensation paid may fluctuate higher or lower than the posted range and the range may be modified in the future due to several factors including, but not limited to, relevant experience, certifications, and qualifications, internal equity, adjustments to the requirements and responsibilities of the job, business needs, and economic and market data. EEO Statement: Middlesex Savings Bank is an Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
Salary Context
This $137K-$250K range is above the median 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 Middlesex Savings, 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. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($193K) sits 9% below the category median. Disclosed range: $137K to $250K.
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.
Middlesex Savings AI Hiring
Middlesex Savings has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Westborough, MA, US. Compensation range: $250K - $250K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 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.
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