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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 Principal Software Engineer for our client who will define the technical vision and lead the design and implementation of AI Cloud’s distributed systems. As a key member of the AI Cloud leadership team, you will partner with principal engineers across the company to architect scalable, reliable, and secure infrastructure that supports millions of developers and thousands of enterprises.
Responsibilities
- Responsibilities Technical Leadership \& Architecture
- + Define and drive the long\-term technical strategy for AI Cloud’s control and data plane services.
+ Architect highly available, multi\-region systems capable of operating seamlessly across multiple cloud providers.
+ Design APIs and service abstractions that integrate Desktop, Hub, and enterprise cloud services.
+ Establish standards for reliability, scalability, and observability across the AI Cloud platform.
+ Lead cross\-functional technical discussions and influence architectural decisions company\-wide.
- Systems Design \& Implementation
- + Design and implement distributed systems for workload orchestration, service discovery, and lifecycle management.
+ Build and operate control plane components that manage multi\-tenant workloads and cloud networking.
+ Develop infrastructure that delivers predictable performance, intelligent scaling, and automated failover.
+ Ensure security, data integrity, and compliance across global infrastructure footprint.
+ Partner with platform and product teams to deliver developer\-friendly APIs and cloud experiences.
- Strategic Impact
- + Align technical direction with business objectives for cloud growth and developer platform unification.
+ Evaluate emerging technologies (e.g., service meshes, container orchestration, edge computing) and guide adoption.
+ Drive initiatives that reduce latency, optimize cost, and improve cross\-cloud performance.
+ Define metrics and SLAs for AI Cloud’s reliability and scalability.
- Leadership \& Mentorship
- + Mentor senior, staff and principal engineers, fostering technical excellence and growth across teams.
+ Lead design reviews and guide critical production system decisions.
+ Drive a culture of operational excellence, ownership, and innovation.
+ Collaborate with engineering and product leadership to align priorities and resource planning.
+ Take part in on\-call rotation for your team; respond to incidents, debug production issues, and drive continuous improvement of system reliability.
Requirements Required
- + 10\+ years of software engineering experience, including 3\+ years in technical leadership roles (Staff or Principal level)
+ Proven experience designing and building highly scalable distributed systems in production environments
+ Deep understanding of cloud infrastructure (AWS, Azure, GCP, or OCI), including compute, networking, and storage primitives
+ Proficiency in Go, Rust, or Java
+ Expertise in Kubernetes, microservices, and service mesh architectures
+ Strong foundation in observability, CI/CD, and infrastructure\-as\-code (Terraform, Pulumi, or CloudFormation)
+ Experience operating high\-availability (99\.99%\+) production systems
+ Exceptional communication skills and ability to influence across technical and business domains
+ Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience
Preferred
- + Experience designing multi\-cloud or cross\-cloud abstractions and orchestration layers
+ Knowledge of container lifecycle management, networking, and policy enforcement
+ Prior experience in developer infrastructure, PaaS, or hyperscale SaaS environments
Background contributing to open source or developer\-focused platforms is a plus.
Benefits
- Work Model – Remote
- Employment Type \- Full\-time
- Salary \- Compensation Range: $232K \- $319K
- 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 $232K-$319K 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 ($275K) sits 17% above the category median. Disclosed range: $232K to $319K.
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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