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About This Role
Welcome to Decision Foundry \- Data Analytics Division!
We are proud to introduce ourselves as a certified "Great Place to Work," where we prioritize creating an exceptional work environment. As a global company, we embrace a diverse culture, fostering inclusivity across all levels.
Originating from a well\-established 19\-year web analytics company, we remain dedicated to our employee\-centric approach. By valuing our team members, we aim to enhance engagement and drive collective success.
We are a leading Data Analytics \& Salesforce consulting firm delivering transformative digital solutions for businesses across industries. Our expert team partners with clients to unlock the full potential of the Salesforce ecosystem, with a specialized focus on data\-driven marketing, automation, and AI\-powered personalization.
We win as an organization through our core tenets. They include:
- One Team. One Theme.
- We sign it. We deliver it.
- Be Accountable and Expect Accountability.
- Raise Your Hand or Be Willing to Extend it
Role summary
We are seeking a Senior Software Engineer to join the new AI Developer Tools team at the forefront of AI\-powered developer productivity for our client who will build cutting\-edge AI agents and tools that transform how developers write code, debug issues, deploy applications, and respond to incidents both internally and for the customers worldwide.
This is an opportunity to work at the intersection of AI and developer experience, building production systems that leverage LLMs and AI agents to accelerate developer workflows. You'll architect and implement AI\-powered tools such as code review assistants, automated test generators, deployment diagnostics agents, and on\-call assistance tools. You'll also contribute to the self\-service platform that enables teams across to rapidly build and deploy their own AI developer tools.
Your work will directly impact how engineers build and operate services powering 20 million users, and as these tools mature, you'll help transform them into commercial offerings for the customers.
You'll collaborate closely with the Principal Engineer on technical architecture, partner with product and design teams on user experience, and work autonomously in a fast\-paced, remote\-first environment where rapid iteration and shipping are core values.
What Would Make Someone Successful In This Role You're passionate about developer experience and excited by the potential of AI to transform how developers work. You have hands\-on experience building production systems with LLMs or AI agents, and you understand the nuances of prompt engineering, agent orchestration, and evaluating AI system effectiveness. You think in terms of products and platforms, balancing technical excellence with pragmatism to ship iteratively while maintaining high quality bars. You're comfortable navigating the rapidly evolving AI/LLM landscape, experimenting with new tools and approaches, and making pragmatic technology choices. You thrive in ambiguity, enjoy rapid prototyping, and have strong opinions on what makes developer tools that engineers love to use. Most importantly, you're collaborative, communicate clearly in remote environments, and take ownership of your work from design through deployment and operations.
Responsibilities
- Build AI\-Powered Developer Tools: Design, implement, and ship production\-ready AI agents and tools that accelerate developer productivity such as code review and refactoring assistants, automated test generators, local environment setup tools, deployment pipeline diagnostic agents, and on\-call assistance tools
- Implement LLM Integrations: Build robust, production\-grade integrations with LLM APIs (OpenAI, Anthropic, etc.) such as prompt engineering, response parsing, error handling, rate limiting, cost management, and performance optimization
- Develop Agent Orchestration Systems: Create agent frameworks and orchestration systems that enable complex multi\-step workflows, tool calling, context management, and agent\-to\-agent communication
- Contribute to Platform Infrastructure: Build self\-service platform capabilities that enable teams across to rapidly deploy and operate their own AI developer tools such as deployment pipelines, observability integration, security controls, and operational tooling
- Drive Adoption of AI\-Native Development: Build tools and programs that accelerate adoption of AI developer tools such as Claude Code, Cursor, and Warp across engineering organization
- Ensure Production Quality: Write well\-tested code with strong test coverage (unit, integration, end\-to\-end); establish monitoring, alerting, and operational excellence for AI systems
- Collaborate Cross\-Functionally: Partner with Principal Engineer on architecture, work with product and design teams on features and UX, and collaborate with platform teams (Infrastructure, Security, Data) on integrations
- Participate in Operations: Take part in on\-call rotation for AI developer tools; respond to incidents, debug production issues, and drive continuous improvement of system reliability
- Mentor and Share Knowledge: Guide other engineers through code reviews, pair programming, and technical discussions; document patterns and best practices for AI tool development
- Measure and Iterate: Instrument AI tools to measure adoption, effectiveness, and developer productivity impact; iterate based on data and user feedback to continuously improve developer experience
- On\-Call Rotation: Take part in on\-call rotation for your team; respond to incidents, debug production issues, and drive continuous improvement of system reliability
Requirements Required:
- 6\+ years building production\-grade backend systems or developer\-facing tools.
- Hands\-on experience with AI/ML technologies such as practical production experience with LLM APIs (OpenAI, Anthropic, etc.), prompt engineering, or AI agent development.
- Proficiency in Go (preferred), Rust, Java, or Python with strong software engineering fundamentals.
- Experience designing and building distributed systems, microservices, or platform infrastructure.
- Strong understanding of cloud\-native systems (AWS, GCP, or Azure), APIs, and data stores.
- Solid grasp of CI/CD, automated testing, code review practices, and modern development workflows.
- Product\-minded approach to building developer tools with focus on user experience and measurable outcomes.
- Excellent communication skills in remote, asynchronous environments with ability to document technical decisions clearly.
- Ownership mentality with bias for action and iterative delivery.
- Comfortable working autonomously across distributed teams and navigating ambiguity.
- Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
Preferred
- Experience with AI agent frameworks (LangChain, LangGraph, CrewAI, or similar).
- Contributions to open source AI tools, developer tooling, or platform engineering projects.
- Experience with MCP (Model Context Protocol) or similar AI agent integration standards.
- Background in developer productivity, DevOps, SRE, or platform engineering domains.
- Experience with Kubernetes, and container orchestration.
- Knowledge of developer tools ecosystems (IDEs, CI/CD platforms, observability tools).
- Experience with infrastructure\-as\-code (Terraform, Pulumi) and GitOps deployment patterns (ArgoCD, FluxCD).
- Understanding of security, compliance, and operational best practices for production AI systems.
Benefits
- Work Model – Remote
- Employment Type \- Full\-time
- Salary \- Compensation Range: $184\.6K \- $260\.7K
- Decision Foundry is an equal opportunity employer and values diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment.
Salary Context
This $184K-$260K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $189K across 518 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 26,159 AI roles we're tracking, AI Software Engineer positions make up 2% of the market. At Decision Foundry, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $235,100 based on 665 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($222K) sits 5% below the category median. Disclosed range: $184K to $260K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Decision Foundry AI Hiring
Decision Foundry has 5 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span Washington, DC, US, New York, NY, US. Compensation range: $85K - $319K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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