Interested in this AI/ML Engineer role at A2Z Sync?
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About This Role
We operate a multi\-tenant automotive SaaS platform serving thousands of dealer groups across the United States, running on a native AWS (NAWS) backend: Lambda, EventBridge, DynamoDB, S3, Step Functions. That platform works. Now we are making it think: production agentic AI systems built code\-first and deployed on AWS Bedrock AgentCore, connected to real systems, making decisions and executing workflows against real dealer data and real money.
This is a hands\-on builder role. Expect the large majority of your time in code. But we are not hiring a pair of hands. Every agent you ship makes decisions that touch dealer revenue, so we need an engineer who understands the business problem before writing the technical solution: someone who asks what a workflow is worth, weighs build cost against dealer impact, and knows when the right answer is a simpler tool, or no agent at all.
What you will own:
Building production agents in Strands/LangGraph on AgentCore: agent logic, tool orchestration, and multi\-agent workflows deployed on AgentCore Runtime with Memory, Identity, and Gateway.
Tool interfaces: framework\-agnostic Python tools, MCP servers, and AgentCore Gateway targets that let agents safely call production APIs.
Evaluation harnesses: offline/online eval pipelines and LLM\-as\-judge CI/CD gates that catch regressions before dealers do.
Guardrails and safe failure: approval gates and rollback paths that determine whether an agent fails safe or fails loud.
Cost and latency management: per\-agent cost tracking and multi\-model routing so each workflow earns more than it costs.
Production observability: AgentCore session tracing, OpenTelemetry, and CloudWatch dashboards for every agent you ship.
Tech environment: Strands Agents SDK on AWS Bedrock AgentCore (Runtime, Gateway, Memory, Identity, Observability); MCP servers; Bedrock Guardrails; Anthropic models (Haiku, Sonnet, Opus); NAWS stack (Lambda, EventBridge, DynamoDB, S3, Step Functions, ECS Fargate, Aurora, API Gateway, CDK); Python primary, Java (Spring Boot) secondary.
You work within the platform's established architectural patterns, and produce deliverables using agentic frameworks, AWS serverless core, MCP/tool design, and observability without someone looking over your shoulder. Prompt engineering and tool\-use design are core engineering disciplines here, not an afterthought.
Scope and scale:
5,000\+ destination dealer tenants, each with isolated databases and per\-tenant configuration.
Agent workflows that touch production transaction data: real dealer money, not a sandbox.
Tens of thousands of API requests across REST, SOAP, and event\-driven integration surfaces that your agents' tools will call into.
A growing portfolio of production agents, each with its own cost, latency, and evaluation budget.
Your first 6 months:
Month 1: Ramp on the platform architecture, existing agents, and integration surfaces, and on the dealer workflows behind them, so you know what the agents are for, not just how they run. Ship your first tool integration (MCP server or Gateway target) to production.
Months 2\-3: Own end\-to\-end agent workflows, from design through production on AgentCore: Strands agent logic, tool interfaces, evaluation harness with CI gates, and guardrails.
Months 4\+: Running autonomously: shipping new agents and tools with minimal oversight, with observability and cost tracking in place for everything you own.
Requirements
Must have:
5\+ years of software engineering experience, including hands\-on production experience building LLM agents in a code\-first framework. Strands Agents and LangGraph are equally acceptable, as are Google ADK, OpenAI Agents SDK, and comparable frameworks. If you have built and operated real agents in any of them, you will pick up Strands quickly.
Business\-conscious judgment: you start from the problem and its value, not the technology, and you have walked away from a clever solution because a simpler one served the customer better.
Strong Python; ability to read and contribute to Java (Spring Boot) services.
Hands\-on experience with AWS serverless (Lambda, EventBridge, DynamoDB, Step Functions).
Experience with prompt engineering, tool/function\-calling design, and evaluating LLM output quality in production.
An appetite for a heads\-down building role: you measure your week in shipped code.
Strongly preferred:
Direct experience with Strands Agents and/or AgentCore Runtime (or migrating agent workloads from Lambda/Fargate to a managed agent runtime); LLM\-as\-judge, agent\-to\-agent orchestration, and agent governance.
Direct production experience with MCP (Model Context Protocol) servers.
Comfort owning production observability (OpenTelemetry, CloudWatch) for the systems you build.
Nice to have:
Automotive, fintech, or multi\-tenant marketplace platform experience.
Familiarity with Bedrock Guardrails, LLM\-as\-judge evaluation, model cost/latency optimization, and multi\-agent orchestration patterns.
Experience with data pipelines (Glue/Athena) or ETL/data lake tooling.
Benefits
About A2Z Sync
A2Z Sync is a fast\-paced and innovative automotive SaaS company seeking to make life better for our customers. We offer you a fun, casual, and collaborative culture, while fostering an environment where you work hard, see your results, and feel your impact. We are committed to our employees, and this starts with providing benefits that allow you to care for you and your family.
Mission
At A2Z Sync, we replace the friction of disconnected systems with the velocity of a single platform. We integrate digital insights with in\-store operations to deliver transparent transactions that bring clarity to the car buyer and increased profitability to the dealer.
Our Values: We Are DRIVEN* Dealership Obsessed: We measure our success by the dealer's wins and the trust of their buyers, not just our own code.
- Relentless Ownership: No lone wolves, but no pass\-backs either. We don't say "that's not my job."
- Invent with Purpose: We don't chase "shiny" tech. We replace guesswork with intelligence, building the "data backbone" that turns raw information into a competitive advantage.
- Value Every Perspective: We are Better Together. We check egos at the door.
- Evolve or Evaporate: Change is our constant. We stay ahead by learning faster than the competition.
- Now Over Next: Perfection is the enemy of progress. We prefer action over endless analysis.
Here’s how we are doing it:* A2Z Sync offers comprehensive medical, dental, and vision benefits.
- Employer provided STD/LTD and life insurance.
- Matching 401k plan.
- Unlimited paid time off, including 10 paid holidays.
- Real ownership of a high\-stakes AI surface — your roadmap, your architecture decisions, your metrics.
- The expected salary range for this role is $160,000 to $190,000 annually, commensurate with experience and qualifications.
- A2Z Sync is an Equal Opportunity Employer and does not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, veteran status, basis of disability or any other federal, state or local protected class.
Salary Context
This $160K-$190K range is above the median 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 A2Z Sync, 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 ($175K) sits 19% below the category median. Disclosed range: $160K to $190K.
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.
A2Z Sync AI Hiring
A2Z Sync has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $190K - $190K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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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