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About This Role
AI Software Engineer
Pulse · Indianapolis, IN · Onsite · Full time
About Pulse
Pulse is an AI transformation and custom development firm based in Indianapolis. We work with mid-market companies, usually 50 to 200+ employees, in manufacturing, financial services, healthcare, nonprofits, and government contracting, plus a few enterprise clients.
Most of these companies haven't started with AI because they think they need perfect data and perfectly run operations first. They don't, and our job is to prove it quickly. A lot of our work starts with a 2-Day AI POC: our engineers sit with a client team, learn how the work actually gets done, and ship a working tool while the strategy work is still happening. 98% of our clients renew.
The Role
You'll own client projects from the first conversation to production. That means finding where AI actually saves time or opens up new revenue, deciding the technical plan, building the whole system, and improving it after it goes live. You'll run multiple projects at a time and work within a very flat organizational structure.
This is a software engineering job first. The model is usually the easy part. What makes a project work is everything around it: clean data models, integrations that don't fall over, tests, monitoring, and code another engineer can pick up. We need people who've built and run real systems, and who now use LLMs as one more tool in that work.
Most projects start with a process someone has run the same way for years. You'll map it step by step until every step is simple, figure out which steps AI can take over, build it, keep a person in the loop where mistakes are expensive, and remove that check once the system has earned trust. A project is done when real hours come off someone's workload.
What You'll Do
- Learn a client's process fast, onsite or remote, often in hours instead of weeks
- Decide where AI helps and where it doesn't, and explain that call to a CEO and to their IT lead
- Build the full system: LLM workflows, agents, integrations with the client's ERP, CRM, email, and files, and the interface people use every day
- Write evals so you know when outputs are good enough to trust
- Ship to production and keep improving it once real users are on it
- Help run 2-Day AI POCs, where the goal is a working tool by the end of day two
- Leave something behind on every project (skills, MCP connectors, eval sets, templates) so the next one starts ahead
What You Won't Do Here
- Sit through three hours of status meetings every Monday
- Wait six weeks for a code review
- Watch your work die in a change management committee
- Babysit a single legacy system for two years
- Write specs for someone else to build
What We're Looking For
- At least 5 years of professional software engineering experience. This is a hard minimum. We want software engineers who can scale with AI, not prompters who pretend to be software engineers.
- You've owned production systems: designed them, shipped them, been on the hook when they broke, and fixed them
- Strong fundamentals in system design, data modeling, testing, and debugging
- Deep experience in Python or TypeScript (ideally both), building and consuming APIs, and working with relational databases like Postgres
- Comfortable deploying and running software in the cloud, including CI/CD, logging, and monitoring
- You've built something on top of LLMs that real people use, and you know the ways it breaks
- Hands-on experience with Claude Code, MCP servers, or agent frameworks
- You can explain a technical decision to a CEO in plain English and to an engineer in full detail
- You like being in front of clients. A big part of this job is listening.
- You'll tell a client something won't work before you build it anyway
Nice to Have
- Integrations with ERPs (Odoo, NetSuite), CRMs (HubSpot), or accounting tools (QuickBooks)
- Consulting, agency, or founder experience
- Background in manufacturing, healthcare, or financial services
What You'll Learn Here
- How to structure work so agents can do it reliably
- Context engineering, meaning how to give a model the right information at the right moment
- How to evaluate outputs and decide where people stay in control
- How to scope work, build a roadmap, and run a client relationship yourself
How We Work
Our values are Unwavering Integrity, Trailblazing Creativity, and Speed as Superpower. In practice:
- We tell clients the truth, including when the answer is "don't use AI for this"
- We try things that haven't been done in a client's industry yet
- We price work off roadmaps instead of hours, so nobody is rewarded for stretching a project out. If it can ship in 3 months instead of 6, it ships in 3.
- We keep the team small on purpose. You'll own more, see more of the business, and your work will be visible to clients and leadership.
What We Offer
- Competitive base salary, commensurate with experience
- Equity in Pulse
- Health, dental, vision
- PTO we actually want you to take
- A real desk in a real office with people who make you better
- Direct access to founders. Sean (CEO) and Jake (COO) are in the trenches with you, not behind a calendar wall.
Details
- Location: Onsite at our office, 350 Massachusetts Ave #256, Indianapolis, IN 46204
- Travel: This position will require travel to client sites
Pay: $120,000.00 - $180,000.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Life insurance
- Parental leave
- Vision insurance
Work Location: In person
Salary Context
This $120K-$180K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $192K across 198 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 9,079 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At PULSE, 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 $220,000 based on 1,601 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($150K) sits 32% below the category median. Disclosed range: $120K to $180K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
PULSE AI Hiring
PULSE has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Indianapolis, IN, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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