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About This Role
About SnapLogic
SnapLogic is the Agentic Integration Company, integrating AI, data, applications, and microservices into one powerful platform that transforms how enterprises connect, automate, and scale. Unlike legacy integration tools, SnapLogic is built for the AI era and trusted by global leaders, including AstraZeneca, Adobe, Verizon, Epsilon and Sony. With its industry\-leading platform, SnapLogic empowers every team across the enterprise to securely build faster, smarter, AI\-connected workflows – all through natural language and intuitive low\-code design. Join the Agentic Integration movement at snaplogic.com.### The Role:
We are looking for a Software Engineer to join our Agent Creator team, focusing on building and maintaining LLM integrations within the SnapLogic integration platform. In this role, you will design and implement AI\-related Snap Packs that connect SnapLogic pipelines to diverse AI models, multimodal platforms, and evolving AI toolchains — enabling customers to build intelligent, enterprise\-grade automation workflows at scale.
You will own the full engineering lifecycle—from system design and prototyping through production deployment and operational excellence. Additionally, you will be a core driver of AI\-assisted development practices within the team, combining your expertise with advanced AI coding agents to accelerate product delivery.
### What You'll Do:
1\. Core Product \& Integration Development
- AI Provider Integrations: Design, build, test, and ship Snap Packs for major AI providers, ensuring robust and high\-performing connections.
- Cross\-Provider Feature Parity: Implement and standardize advanced LLM capabilities across different providers, including Structured Outputs, Reasoning Models, Function Calling, Background Mode, and Vector Store integrations.
- Agent Framework \& MCP: Develop and maintain the SnapLogic Agent Framework to support complex agentic workflows (incorporating iteration control, parallel tool calls, and observable execution via Agent Visualizer). Contribute to the Model Context Protocol (MCP) Server platform, including lifecycle management, observability, and registry.
2\. Engineering Excellence \& AI\-Assisted Development
- AI\-Augmented Coding: Leverage AI coding agents to write well\-crafted, testable, and maintainable code, while maintaining full ownership, deep understanding, and accountability for the AI\-generated codebase.
- Internal AI Innovation: Lead internal AI\-driven initiatives to accelerate team velocity; rapidly prototype, validate, and productionalize internal AI tools (e.g., building dedicated AI Agents to automate Snap development).
- Code Quality \& Operations: Write clean, structured, and testable Java/Python code adhering to checkstyle standards, maintaining a 90%\+ unit test coverage target. Participate in code reviews and collaborate with QA/Release teams to validate builds across the production environment.
3\. Strategy \& Knowledge Sharing
- Trend Adoption: Stay at the forefront of the rapidly evolving AI ecosystem, selectively landing cutting\-edge capabilities into the SnapLogic product line to deliver immediate customer value.
- Evangelism \& Documentation: Institutionalize project learnings into high\-quality technical documentation. Share knowledge through internal demos and evangelize engineering and AI best practices across the organization.
### What You'll Bring:
- Experience \& Education: Bachelor’s degree with a minimum of 2 years of related experience, or an advanced degree, or equivalent practical work experience.
- Agentic \& AI Patterns: Strong foundational understanding of agentic design patterns (tool use, agent loops, function calling, structured outputs, reasoning models).
- Frameworks \& APIs: Robust understanding of MCP (Model Context Protocol) or AI agent orchestration frameworks. Hands\-on experience with LLM APIs (OpenAI, Azure OpenAI, Google Vertex AI, or Amazon Bedrock; Anthropic experience is highly preferred).
- Backend \& Data Skills: Solid experience building or consuming REST APIs and a strong command of JSON Schema and structured data validation.
- Engineering Persona: \* Attention to Detail: Deep care for edge cases, comprehensive error handling, and intuitive user\-facing validation/lint messages.
+ Ambiguity Thriver: Ability to quickly self\-learn, synthesize information, and drive towards a solution when facing ambiguous problems outside your immediate expertise.
+ Collaboration: Strong cross\-functional communication skills to work seamlessly across backend, platform, and UI teams.
### Nice to Have:
- Experience with SnapLogic or similar iPaaS (Integration Platform as a Service) / enterprise integration platforms.
- Familiarity with Maven\-based build systems and modern CI/CD pipelines.
- Python experience (ideally for developing platform\-layer components).
The above range is the approximate annual U.S. base pay range for this position. Final offer amounts are determined by multiple factors, including candidate location, experience and expertise, and may vary from the range listed. In addition to base salaries, certain roles are also eligible for annual cash bonuses or commissions. All of our full time employees receive a comprehensive benefits package.Why Join:
There's never been a better time to join our SnapSquad!
At SnapLogic, we believe in empowering people \- customers and employees alike \- to integrate everything and create anything. From competitive salaries and equity packages to global wellness benefits, we’re committed to your success and well\-being. A Few Reasons You’ll Love it Here: We’re Innovators
SnapLogic pioneered the first generative integration solution, SnapGPT, and continues to lead with a full suite of AI\-powered tools \- making integration faster, smarter, and accessible to more people. We’re Recognized Leaders
From being named a Visionary in multiple Gartner Magic Quadrants, leading the market in innovative AI reports from Aragon Research, or being recognized for AI in the Cloud Awards, we’re setting the pace in a rapidly evolving market. We’re Growing Fast
Named one of Inc. 5000’s Fastest Growing Private Companies in 2024, SnapLogic is scaling globally \- and we want you to grow with us. We’re AgenticOur platform empowers everyone across the enterprise to create automated, AI\-connected workflows. That means more impact, less friction, and a bigger role for YOU in driving transformation. Are you ready to help the world integrate everything and create anything? Let’s talk. Apply now and help shape the future of integration.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $120K-$140K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $183K across 194 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 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At SnapLogic, 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 $219,250 based on 424 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($130K) sits 41% below the category median. Disclosed range: $120K to $140K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
SnapLogic AI Hiring
SnapLogic has 1 open AI role right now. They're hiring across AI Software Engineer. Based in San Mateo, CA, US. Compensation range: $140K - $140K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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