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About This Role
Job Summary
Greetings from FutureStack AI!
We're a fast\-moving and highly\-motivated startup with our first AI Agent ( for a Real Estate automation workflow ) currently in Private Beta.
We're building AI agents and a cutting\-edge CRM for real estate professionals on a LangGraph \+ Claude \+ Drupal stack. We need an engineer who has actually shipped agents to real users — not built demos, not written papers, *shipped*. You'll be the second engineer on the agent team, making architectural calls that hit production within days.
Our mission is to create "smarter" agentic systems \- ones that learn from feedback, can efficiently orchestrate teams of agents, all supported by a 4\-layer memory system that includes the Agent context, perpetually updated and versioned MD files, a Drupal CMS \- with it's relational database, JSON API, AI Agents logging / monitoring, and content versioning capabilities \- and a Postgres Vector DB that integrates with the Drupal plsatform. A primary orchestration agent will be run
If you've ever written a multi\-hundred\-line LangGraph supervisor and watched it survive a Twilio webhook storm, we want to talk to you.
The Role
This is a hands\-on, ship\-every\-week role. Day\-to\-day:
- Writing LangGraph state machines with checkpointing, conditional routing, replan loops, and cost guards — graphs that run for hours and survive crashes
- Wiring multi\-model routing across Claude Opus, Sonnet, GPT\-5, and local Ollama models. Every call gets a cost\-vs\-quality decision.
- Building memory systems where agents actually accumulate expertise — not just bolting on a vector DB and calling it done
- Shipping production SMS conversation flows with structured extraction, bilingual prompts, decision logs you can audit a week later. No silent failures.
- Building MCP servers that bridge Drupal, vector search, and external tools to the agent layer
- Owning observability — Langfuse traces, per\-node spans, cost\-per\-task tracking, alerting. If we can't see it, it's broken.
- Deploying to AWS ECS Fargate with Bedrock, with local Docker Compose on Mac Studio M2 Ultra for development
Who You Are
Required
- 3\+ years professional AI engineering, with at least one agent or LLM system that real users touched in production
- Python fluency: async, type hints, FastAPI, SQLAlchemy, Pydantic, Docker
- Comfortable with the full LLM lifecycle: prompt design, structured outputs, evaluation, cost optimization, failure modes
- Postgres at the schema level, not just the ORM level
- A portfolio or GitHub that shows actual shipped work
Strongly Desired
- Advanced degree (MS/PhD) in AI, ML, CS, or a related field — or 5\+ years of equivalent industry depth
- 3–5\+ years as an AI/ML engineer specifically (not "I added an LLM call to a Rails app once")
- Production LangGraph — StateGraph, conditional routing, checkpointing, supervisor patterns, subgraphs. If you've written more LangGraph than the official tutorials, you're our person.
- MCP (Model Context Protocol) servers and tool integration
- pgvector with HNSW indexing for semantic memory at production scale
- Multi\-model orchestration across Claude \+ GPT \+ open\-source models
- LLM observability — Langfuse, LangSmith, or equivalent
- Twilio / 10DLC / SMS conversational state machines
- Drupal (or willingness to learn it fast — we use it as the agent management hub, not a legacy burden)
- Bilingual Spanish — we serve a bilingual South Florida market
- DSPy or other prompt\-optimization frameworks
- Open\-source contributions to LangChain, LangGraph, MCP, or related projects
- You've debugged a production agent at 2am and lived to write the postmortem
What You Bring
- A bias for shipping. We value working code over architecture diagrams.
- Comfort owning ambiguous problems end\-to\-end — you'd rather build something and iterate than wait for a perfect spec.
- Strong opinions about agent design, loosely held.
- Honest engineering: you fail loudly, you don't paper over LLM errors with silent fallbacks, and you write tests inline rather than batching them at the end.
How We Work
- Spec → code → progress doc. Every project has a markdown spec and a live checklist. If the spec is wrong, we fix the spec first.
- Inline tests, never batched. Phase 1 ships with Phase 1's tests. No "we'll add tests at the end."
- Fail loudly. No silent fallbacks, no swallowed exceptions. If the LLM errors, the turn fails and the user retries.
- Founder writes code daily. You'll review the founder's PRs. The founder will review yours. There is no buffer layer.
- Small team, big surface area. Your name will be on the architecture, not buried in commit history.
Why You Might Want This
- Get to implement a wide variety of Agentic AI workflows. You'll learn the orchestration core deeply because it powers everything.
- Real customers, fast feedback loops, no theater.
- The founder ships. You won't be waiting on PRs to merge for two weeks.
Why You Might Not
- We're a startup. The roadmap moves. If you need a 6\-month locked spec, this isn't it.
- We use Drupal. It's a deliberate architectural choice for HITL workflows and config management — not a legacy burden — but if PHP gives you hives, you'll be unhappy.
- You'll be on call for production issues sometimes. Not constantly, but sometimes.
What We Offer
- Meaningful early\-stage equity — this is a senior engineering role at a pre\-revenue company, and the equity reflects that
- Revenue share on products you help ship
- Salary after funding — which you'll have a direct hand in making happen
- Direct ownership of architecture decisions on two live products
- Cloud credits, hardware budget, and full AI tooling
- Remote\-first, with optional in\-person time in South Florida
- A founder who ships and respects engineers who ship
To Apply: Send over your resume with your GitHub, a link to something you've built, and one paragraph on the hardest agent bug you ever debugged. Skip the generic cover letter.
Fun Question: What percentage of Pied Piper equity did Danesh and Gilfoyle each own ( how many points ) ?
Pay: $50,000\.00 \- $250,000\.00 per year
Work Location: Remote
Salary Context
This $50K-$250K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At FutureStack AI Inc, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($150K) sits 10% below the category median. Disclosed range: $50K to $250K.
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.
FutureStack AI Inc AI Hiring
FutureStack AI Inc has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $250K - $250K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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