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About This Role
Job Overview
Join the Enterprise Applied AI team to build and ship production AI experiences and platforms that help internal teams work smarter and faster. As an AI Engineer, you will contribute across the stack, from data pipelines and retrieval to prompt/agent logic, evaluation/guardrails and serving. You will collaborate closely with partners across Operations, Product, Sales, Customer Service, Finance, HR, and other internal teams to understand needs and deliver practical solutions that create tangible business value. You will develop responsibly, partnering with governance stakeholders on privacy, security, compliance and safety.
Job Description:
- Build features and services across the AI stack: orchestration, retrieval/grounding, prompt/agent logic, evaluation/guardrails, serving and observability.
- Implement robust data processing and integration pipelines to enable high\-quality AI applications and analytics.
- Contribute to design docs, code reviews, testing, and operational playbooks to ensure reliability, maintainability and resilience.
- Partner with product and business stakeholders to define requirements, iterate quickly and measure outcomes using clear success metrics.
- Instrument telemetry and evaluation to monitor quality, safety, latency and cost; improve performance based on data.
- Follow responsible AI practices for security, privacy, compliance and safety in collaboration with governance teams.
- Participate in on\-call and incident response rotations as appropriate; drive post\-incident improvements.
- Share learnings via demos and documentation; contribute to AI literacy and enablement across the org.
Minimum Requirements:
- BS/MS in Computer Science or a related field, or equivalent experience.
- Practical software engineering experience building backend services, APIs or data\-intensive applications; strong foundations in algorithms, data structures and systems.
- Exposure to or hands\-on experience with LLM application concepts such as retrieval, grounding, prompt/agent, design, function/tool use, evaluation, safety/guardrails and cost/latency optimization.
- Proficiency with modern software delivery practices (version control, CI/CD, testing, observability); familiarity with cloud\-native services and containerization.
- Ability to collaborate with product and business partners; strong written and verbal communication skills.
- Bias to ship, learn and iterate; comfortable working in fast\-evolving technology areas with incomplete information.
- Demonstrated ownership of services or platform components, end\-to\-end delivery of cross\-service initiatives and contributions to reliability/SLOs and operational excellence.
- Agentic Workflows \& Memory Systems
\> Stateful Orchestration: Building and debugging production\-grade, cyclic multi\-agent workflows and state machines.
\> Context \& Memory Engineering: Implementing multi\-layered memory architectures including short\-term memory for active turn execution, long\-term memory for cross\-session state persistence, and episodic summarization to manage token context windows. \> Tool Call Management: Designing dependable function\-calling patterns equipped with automated retry logic and self\-correction handlers.
Data \& Retrieval Infrastructure
\> Vector \& Relational Storage: Managing relational metadata schemas and executing optimized semantic vector similarity searches inside a combined relational database layer.
\> Document Persistence: Utilizing document\-store databases to store unstructured execution payloads, dynamic agent states, and raw chat logs.
\> Hybrid RAG Pipelines: Combining relational/exact\-match queries with vector\-space searches for high\-precision retrieval.
Production Reliability \& Performance
\> Granular Tracing: Instrumenting end\-to\-end tracing to monitor agent execution steps, debug non\-deterministic loops, and track token costs.
\> Automated Evals: Creating programmatic evaluation testing and scoring frameworks to benchmark agent accuracy before production deployment.
\> Core Backend Development: Writing clean, concurrent, asynchronous Python code to handle high\-throughput foundation model APIs.
Work Location: CA, potentially remote
Pay: $115,000\.00 \- $130,000\.00 per year
Work Location: Remote
Salary Context
This $115K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At intent design ltd, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($122K) sits 43% below the category median. Disclosed range: $115K to $130K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
intent design ltd AI Hiring
intent design ltd has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $130K - $130K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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