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About This Role
The Role
We are seeking a hands\-on Senior Software Developer / Technical Lead with an AI focus to help us lead teams that build new applications and modernize existing ones for our clients. You will work as part of an AI Led Application Modernization team that believes the way software is designed, built, tested, and maintained is changing quickly—and that senior technical leaders have an opportunity to shape how teams adopt these changes responsibly and effectively. Among your responsibilities will be:
- Set technical direction for delivery teams, including architecture, engineering practices, modernization approach, AI adoption patterns, and quality expectations.
- Remain hands\-on in the work by designing, coding, reviewing, testing, and troubleshooting software while helping other developers deliver well\-architected solutions.
- Use AI\-assisted development tools and create fit\-for\-purpose agents and agentic workflows using frameworks such as LangGraph, A2A, MCP\-enabled tools, or similar approaches when they are the right solution for a client or delivery challenge.
This role is both hands\-on and leadership oriented. You will write code, review AI\-generated output, guide architectural decisions, mentor developers, establish delivery patterns, and help teams use AI responsibly to build and modernize software faster without sacrificing quality, security, or maintainability.
Key Responsibilities
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- Lead a team of developers through new application builds and modernization programs while remaining actively involved in design, coding, review, testing, and troubleshooting
- Set technical direction for projects, including architecture, integration patterns, modernization strategy, engineering standards, delivery approach, and technical risk management
- Use AI\-assisted development tools to accelerate code comprehension, generation, refactoring, testing, documentation, and migration planning while ensuring that outputs are validated by experienced engineering judgment
- Architect and build fit\-for\-purpose agents and agentic workflows using frameworks such as LangGraph, A2A, MCP\-enabled tools, or comparable technologies
- Guide modernization of existing applications by analyzing legacy code, identifying business logic, assessing constraints, defining target architecture, and creating incremental transformation roadmaps
- Make pragmatic architecture decisions across APIs, data models, cloud services, security, observability, integration, DevSecOps, and user experience considerations
- Mentor developers in software engineering practices, AI\-assisted delivery techniques, secure coding, automated testing, maintainable design, and effective code review
- Collaborate with architects, product owners, business stakeholders, client technical teams, and delivery leaders to translate business needs into executable technical plans
- Identify and manage risks in AI\-generated software, including brittle code, hidden assumptions, weak tests, security issues, licensing concerns, maintainability gaps, and architecture drift
- Create reusable patterns, accelerators, prompts, agents, reference architectures, and engineering practices that help the broader team deliver modernization work more effectively
Who You Are
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You are a senior developer and technical leader who still enjoys building useful software and solving hard problems. You have seen enough delivery challenges to recognize patterns, anticipate risks, and help a team choose the right architecture and engineering approach for the situation.
You have led developers through either new software development projects, application modernization efforts, or both. You know how to establish technical direction, break complex work into deliverable increments, coach developers through tradeoffs, and keep the team focused on building software that is secure, maintainable, testable, and aligned to business outcomes.
You are excited by the changes coming to software development. You have a growth mindset, actively experiment with AI\-assisted development tools, and are interested in creating practical agents, workflows, and engineering practices that help teams build, understand, modernize, and operate software more effectively.
Required Technical and Professional Expertise
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- Significant hands\-on software development experience delivering production\-quality applications
- Experience leading a team of developers, including mentoring, code review, technical planning, delivery guidance, and issue resolution
- Experience setting technical direction for software delivery projects, including architecture decisions, engineering standards, delivery patterns, and technical risk management
- Experience with at least one modern programming language such as Java, Python, JavaScript/TypeScript, C\#, Go, or comparable technologies
- Experience with either new application development, application modernization, or both
- Practical experience using AI\-assisted development tools to support coding, refactoring, testing, documentation, debugging, code comprehension, and architecture exploration
- Experience reviewing and validating AI\-generated code for correctness, security, maintainability, performance, testability, and alignment with architecture and requirements
- Familiarity with agentic development concepts and experience building or prototyping fit\-for\-purpose agents using LangGraph, A2A, MCP\-enabled tools, or similar frameworks
- Strong understanding of software architecture and engineering fundamentals, including APIs, data models, integration patterns, automated testing, version control, CI/CD, secure coding, observability, and operational support
- Ability to analyze existing codebases, identify modernization opportunities, define target\-state architecture, and create pragmatic incremental transformation plans
- Ability to communicate technical direction clearly to developers, architects, stakeholders, and client teams
- Growth mindset and enthusiasm for how AI will reshape the software development profession
Preferred Technical and Professional Experience
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- Experience modernizing legacy applications, including mainframe, Fortran, J2EE, ASP, monolithic, or other enterprise application estates
- Experience architecting cloud\-native applications, APIs, microservices, event\-driven systems, or modern front\-end experiences
- Hands\-on experience with LangGraph, A2A, MCP, OpenAI or Anthropic APIs, Semantic Kernel, CrewAI, or comparable agent and orchestration frameworks
- Experience designing agents that interact with enterprise tools, repositories, documentation, tickets, CI/CD pipelines, runtime telemetry, or application data
- Experience defining architecture guardrails, reference implementations, coding standards, reusable components, or engineering playbooks for delivery teams
- Familiarity with DevSecOps practices, automated quality gates, observability, containerization, infrastructure as code, and secure software delivery pipelines
- Experience with retrieval\-augmented generation, vector databases, tool calling, evaluation harnesses, prompt engineering, LLM application testing, or agent observability
- Experience working in Agile delivery environments with product owners, architects, business stakeholders, client executives, and distributed engineering teams
Ability to mentor and influence other developers in practical, responsible, and effective use of AI\-assisted software development practices
Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 4,317 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Delan Associates, Inc, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $217,100 based on 471 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Delan Associates, Inc AI Hiring
Delan Associates, Inc has 9 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer. Positions span Remote, US, Charlotte, NC, US, Fort Lauderdale, FL, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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