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About This Role
Full\-Stack AI EngineerPosition Type: Full\-Time, Remote
Working Hours: U.S. Business Hours
Location: Remote (LATAM, Eastern Europe, Pakistan, India, South Africa Preferred)
About the Role
We are hiring a highly skilled Full\-Stack AI Engineer to build, deploy, and scale AI\-powered applications that solve real business problems.
This role combines full\-stack software engineering with applied AI/ML expertise. You will work across backend systems, AI pipelines, APIs, cloud infrastructure, and frontend applications to bring AI features from prototype to production.
The ideal candidate is both technically strong and product\-minded — someone who can move quickly, build scalable systems, and turn modern AI capabilities into reliable, user\-friendly products.
You will collaborate closely with engineering, product, and data teams to deliver AI\-powered workflows, intelligent automation systems, chat experiences, analytics tools, and scalable machine learning infrastructure.
What You’ll OwnAI \& LLM Integration* Deploy and integrate AI/ML models using OpenAI, Hugging Face, TensorFlow, PyTorch, or similar frameworks
- Build scalable APIs for AI inference using FastAPI, Flask, or Node.js
- Develop retrieval\-augmented generation (RAG) pipelines using Pinecone, Weaviate, FAISS, or vector databases
- Implement embeddings, semantic search, and AI\-powered workflows
- Optimize inference performance, latency, and cost efficiency
Full\-Stack Application Development* Build frontend interfaces using React, Next.js, Vue, or modern JavaScript frameworks
- Develop backend systems and APIs that connect AI models with business logic
- Create user\-facing AI features such as chatbots, copilots, dashboards, and automation tools
- Ensure applications are responsive, secure, scalable, and production\-ready
- Build microservices and scalable backend architectures
Data Engineering \& Pipelines* Develop ETL pipelines for ingesting, cleaning, transforming, and managing datasets
- Automate preprocessing, data labeling, and workflow orchestration using Airflow, Prefect, or Dagster
- Manage structured and unstructured datasets in cloud environments
- Maintain reliable pipelines for model training, fine\-tuning, and evaluation
Infrastructure, DevOps \& MLOps* Containerize AI services using Docker and deploy applications using Kubernetes or cloud infrastructure
- Build CI/CD pipelines for model deployments and application releases
- Monitor model performance, drift, costs, and system reliability
- Work with cloud platforms such as AWS, GCP, Azure, Vertex AI, or SageMaker
- Improve scalability, uptime, and infrastructure efficiency
Security, Compliance \& Reliability* Implement secure API authentication, access control, and rate limiting
- Ensure AI systems comply with GDPR, HIPAA, SOC 2, or related compliance requirements
- Maintain monitoring, logging, and observability for production systems
- Troubleshoot production incidents and optimize system reliability
Collaboration \& Product Development* Partner with product and data teams to define AI\-powered product features
- Translate AI prototypes into scalable production systems
- Participate in sprint planning, technical discussions, and architecture decisions
- Maintain clear technical documentation and reproducible workflows
What Makes You a Great Fit* You are both a strong software engineer and a hands\-on AI builder
- You enjoy shipping AI\-powered features that solve real\-world business problems
- You are comfortable moving from prototype to production independently
- You think critically about scalability, performance, cost, and usability
- You stay current with rapidly evolving AI tools, frameworks, and infrastructure
- You communicate clearly and collaborate effectively across technical and non\-technical teams
Required Experience \& Skills* 3\+ years of software engineering experience with AI/ML exposure
- Strong proficiency in Python and JavaScript/TypeScript
- Experience with AI/ML frameworks such as PyTorch or TensorFlow
- Experience deploying ML or LLM systems into production environments
- Strong frontend experience with React, Next.js, or Vue
- Experience building APIs and backend services
- Strong SQL skills and experience with cloud data platforms
- Familiarity with Docker, CI/CD pipelines, and cloud deployments
Preferred Experience* Experience building AI\-powered SaaS platforms or automation products
- Experience with LLM fine\-tuning, embeddings, and RAG systems
- Familiarity with vector databases and semantic search infrastructure
- Experience with MLOps tools such as MLflow, Kubeflow, Vertex AI, or SageMaker
- Knowledge of microservices, serverless architectures, and distributed systems
- Experience optimizing inference cost and performance at scale
What a Typical Day Looks Like
A Full\-Stack AI Engineer’s day revolves around building production\-ready AI systems and scalable applications. You will:
- Build and optimize AI\-powered APIs and backend services
- Develop frontend interfaces for AI\-driven experiences and workflows
- Maintain data pipelines and model integration systems
- Monitor production environments for performance, uptime, and cost efficiency
- Collaborate with engineering and product teams to prioritize and ship AI features
- Troubleshoot system bottlenecks and continuously improve scalability and reliability
In short: you help transform AI capabilities into scalable, production\-grade products that drive real business impact.
Key Metrics for Success (KPIs)* Successful deployment of AI\-powered features on schedule
- Application uptime and infrastructure reliability maintained at high standards
- Fast and stable inference performance for production endpoints
- Reduction in manual workflows through AI automation
- Strong adoption and usage of AI\-powered product features
- Scalable, maintainable, and cost\-efficient system architecture
Interview Process* Initial Phone Screen
- Video Interview with Pavago Recruiter
- Technical Assessment (AI API \+ Full\-Stack Integration Exercise)
- Client Interview with Engineering Team
- Offer \& Onboarding
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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 3,963 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Pavago, 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 $180,000 based on 12,398 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $163,400.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
Pavago AI Hiring
Pavago has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $218,800 across 509 tracked positions. That's 9% above the national median.
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 3,963 open positions tracked in our dataset. By seniority: 116 entry-level, 1,875 mid-level, 1,532 senior, and 440 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (593 positions). The remaining 3,349 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 3,963 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,783), Data Scientist (297), AI Software Engineer (279). 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 (116) are outnumbered by mid-level (1,875) and senior (1,532) 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 440 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (593 positions), with 3,349 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $290,000 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 (2,043 postings), Aws (1,241 postings), Azure (934 postings), Rag (886 postings), Gcp (774 postings), Pytorch (614 postings), Prompt Engineering (614 postings), Claude (564 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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