Interested in this AI/ML Engineer role at Pond & Company?
Apply Now →Skills & Technologies
About This Role
Ready to turn your ambition into achievement? Join Pond \& Company and be part of the team that’s redefining the future of Architectural, Engineering, Planning, Construction Management, and Environmental projects!
Position Overview
Pond \& Company / Enercon Services, Inc. is seeking a visionary, execution\-focused Director of Artificial Intelligence \& Software Engineering to lead the strategy, development, and delivery of enterprise AI capabilities and software engineering initiatives across a growing multidisciplinary engineering, architecture, planning, environmental, and consulting organization of approximately 2,700 professionals.
This leader will be responsible for building and scaling the organization's AI and software engineering functions, driving digital transformation, intelligent automation, enterprise application development, and AI\-enabled innovation. The role requires a unique combination of strategic leadership, organizational influence, technical depth, and operational excellence.
The Director will oversee the development of enterprise AI platforms, agentic systems, software products, automation frameworks, integrations, and digital solutions that improve operational performance, engineering productivity, project delivery, knowledge management, and client outcomes.
Working closely with executive leadership, business units, operations, IT, cybersecurity, data, and engineering teams, this individual will help establish AI and software engineering as core strategic capabilities and competitive differentiators for the organization.
Key Responsibilities
Enterprise AI \& Software Engineering Strategy
- Develop and execute a comprehensive enterprise strategy for Artificial Intelligence, software engineering, intelligent automation, and digital product development.
- Establish multi\-year roadmaps for:
- AI adoption and enablement
- Enterprise software platforms
- Digital transformation initiatives
- Agentic AI systems
- Intelligent automation
- Knowledge management solutions
- Partner with executive leadership to align technology investments with business objectives and growth strategies.
- Identify opportunities to leverage AI and software solutions to improve operational performance, project delivery, client service, and innovation.
- Drive measurable business outcomes through technology initiatives focused on productivity, scalability, quality, and efficiency.
- Serve as a trusted advisor and thought leader on AI, software engineering, and emerging technologies.
AI Platform \& Intelligent Systems Leadership
- Lead development and deployment of enterprise AI capabilities leveraging:
- Large Language Models (LLMs)
- Retrieval\-Augmented Generation (RAG)
- AI agents and multi\-agent systems
- Workflow automation
- Predictive analytics
- Knowledge management platforms
- AI\-assisted engineering and design solutions
- Establish standards for AI architecture, governance, observability, scalability, and lifecycle management.
- Evaluate emerging AI technologies and guide adoption of enterprise\-ready solutions.
- Ensure AI systems are secure, governed, maintainable, and aligned with organizational standards.
- Oversee AI platform architecture and integration with enterprise systems.
Software Engineering Leadership
- Lead and grow a high\-performing software engineering organization responsible for enterprise applications, integrations, automation solutions, digital products, and AI\-enabled platforms.
- Establish software engineering standards, development methodologies, architecture principles, and delivery frameworks.
- Drive adoption of modern engineering practices including:
- Agile development
- DevOps
- CI/CD
- Cloud\-native architectures
- API\-first development
- Platform engineering
- Oversee software architecture reviews and technical governance.
- Ensure software solutions meet performance, reliability, security, and scalability requirements.
- Guide build\-versus\-buy decisions and vendor technology evaluations.
Team Leadership \& Organizational Development
- Recruit, develop, mentor, and retain AI engineers, software engineers, architects, developers, and technical leaders.
- Build organizational capabilities in AI, software engineering, automation, and digital innovation.
- Foster a culture of collaboration, innovation, accountability, and continuous improvement.
- Establish career development frameworks and technical leadership pathways.
- Manage budgets, resource planning, staffing strategies, and vendor relationships.
Business Transformation \& Digital Innovation
- Partner with operational and business leaders to redesign workflows and business processes through AI and software solutions.
- Identify opportunities to improve:
- Project delivery efficiency
- Engineering productivity
- Knowledge management
- Proposal generation
- Document processing
- Data analytics and reporting
- Resource planning
- Client engagement
- Field operations
- Lead digital transformation initiatives from ideation through enterprise deployment.
- Develop frameworks for prioritizing technology investments based on business value, ROI, risk, and strategic alignment.
Governance, Security \& Risk Management
- Establish governance frameworks for AI systems, software development, and enterprise applications.
- Partner with cybersecurity, legal, compliance, and risk management teams to ensure responsible technology deployment.
- Develop policies related to:
- AI governance
- Data privacy
- Software security
- Intellectual property protection
- Third\-party technology management
- Responsible AI practices
- Ensure compliance with regulatory requirements, client expectations, and industry standards.
Qualifications
Required
- Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Engineering, Data Science, or a related field.
- 10\+ years of progressive leadership experience in software engineering, technology, AI, digital transformation, or related disciplines.
- 5\+ years of leadership experience managing software engineering teams, technical organizations, or digital product development functions.
- Demonstrated success leading enterprise software development and AI initiatives from strategy through implementation.
- Strong expertise with modern software engineering and AI technologies, including:
- Enterprise application development
- Cloud\-native architectures
- APIs and integrations
- DevOps and CI/CD
- LLMs and Generative AI
- AI agents and orchestration frameworks
- RAG architectures
- Vector databases
- Workflow automation platforms
- Cloud AI services
- Experience leading cross\-functional enterprise initiatives and organizational change efforts.
- Strong technical credibility with the ability to engage deeply with engineers, architects, and executive stakeholders.
- Experience managing technology budgets, vendor relationships, and strategic technology investments.
- Proven ability to balance strategic leadership with operational execution.
Preferred
- Master’s degree in Computer Science, Engineering, Business Administration, Data Science, or related field.
- Experience within engineering, architecture, construction (AEC), energy, utilities, environmental, industrial, or professional services organizations.
- Experience building and scaling internal software engineering teams.
- Experience implementing enterprise AI platforms, copilots, knowledge systems, or intelligent automation solutions.
- Familiarity with Azure AI, OpenAI, Microsoft Copilot ecosystem, AWS AI/ML services, LangChain, LangGraph, vector databases, and enterprise automation platforms.
- Experience leading enterprise modernization or digital transformation programs.
Leadership Competencies
- Strategic and forward\-thinking technology leader
- Strong execution and operational discipline
- Exceptional people leadership and talent development skills
- Entrepreneurial mindset with a bias toward innovation
- Collaborative and influential across all organizational levels
- Strong systems\-thinking and problem\-solving abilities
- Technically credible and business\-oriented
- Effective communicator capable of translating technology investments into business value
- Comfortable leading through ambiguity and organizational change
Success Metrics
Success in this role will be measured by:
- Enterprise adoption and impact of AI and software solutions
- Delivery of strategic software engineering initiatives
- Operational efficiency improvements across the organization
- Productivity gains enabled through AI and automation
- Quality, reliability, and scalability of enterprise applications
- AI governance and software engineering maturity
- Innovation pipeline development and execution
- Business value realization and ROI from technology investments
- Growth and retention of high\-performing engineering teams
- Advancement of digital transformation objectives
All Pond \& Company positions require participation in at least one in\-person interview as part of our hiring process, and most roles also require in\-person onboarding at a Pond office location; candidates should expect to meet directly with our team and should be cautious of any communication suggesting otherwise.
About Pond
Pond is an award\-winning, full\-service architecture, engineering, planning, construction management, and environmental services firm providing professional solutions to clients throughout the U.S. and globally for nearly 60 years. Pond’s staff of 800\+ professionals provide a deep bench strength of experience and capabilities to offer customized solutions that help clients manage projects from concept to completion – and everything in between – with confidence and clarity. Pond is currently ranked as the 80th largest engineering and design firm by ENR, Atlanta’s \#1 engineering firm by Atlanta Business Chronicle, and has been recognized as an Employer of the Year by Georgia ACEC and a Best Place to Work for Working Parents.
Additional Information
Many factors are considered when determining compensation at Pond, including scope and level of position, geographic location, candidate skill, knowledge and experience. Starting base pay may vary depending on these factors. Please see the hiring range associated with this posting for more information.
Additional cash incentives may be provided as part of the compensation package, in addition to a range of medical, financial and/or other benefits dependent on position offered. Learn more about Pond’s comprehensive benefits offerings here.
All offers of employment made by Pond \& Company are contingent upon satisfactory background check results. Pre\-employment background checks will be conducted on all candidates that are offered a position at Pond in compliance with program policy as well as state and federal regulations. Additionally, offers may be contingent upon the successful completion of a pre\-employment drug and alcohol test.
Equal Opportunity Employer
We are an equal opportunity and affirmative action employer that recognizes the value of diversity and inclusion in the workplace. Employment decisions at Pond are based on business needs, job requirements and individual qualifications. All suitably qualified applicants will receive consideration for employment. We prohibit discrimination and harassment of any kind based on race, color, sex, age, religion, sexual orientation, national origin, disability, genetic information, pregnancy, or any other protected characteristic as outlined by federal, state and local laws in jurisdictions where we operate. If you need assistance or an accommodation due to a disability, you may contact us at [email protected].
Apply for this position at https://www.pondco.com/careers.We are always looking for driven professionals of all disciplines to join our fast\-growing company. For more information on our services, clientele, or employment opportunities, visit our website at www.pondco.com.
Should you run into any issues in completing the application, please reach out to [email protected] for assistance.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Pond & Company, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Pond & Company AI Hiring
Pond & Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Peachtree Corners, GA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.