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About This Role
Senior AI QA Platform Engineer – Agentic QA Orchestration (Amazon Bedrock)
Company: BONbLOC Technologies
Location: Remote (Candidates must reside in or around Chicago Area, Midwest, New Jersey, or New York)
Role Type: Full\-Time / Contract
Seniority Level: Senior Level (7\+ Years Experience)
Position Summary
We are seeking a Senior AI QA Platform Engineer to design and implement an enterprise AI\-enabled QA Orchestration Platform leveraging Amazon Bedrock, AgentCore, Playwright, AWS CodeBuild, GitHub, Databricks, Jira, and qTest.
This platform will utilize agentic AI workflows to generate and maintain automated tests, orchestrate execution paths, classify test failures, create actionable defects, and provide governance, observability, traceability, and auditability across the QA lifecycle. The ideal candidate combines expertise in software engineering, cloud\-native platforms, AI/LLM integration, CI/CD automation, and quality engineering.
Primary Responsibilities
- Agentic AI Orchestration: Design and implement AI\-powered QA orchestration workflows using Amazon Bedrock, AgentCore, and Multi\-Agent Architectures.
- Autonomous QA Capabilities: Develop agent\-based automation capabilities for test generation, self\-healing test maintenance, failure classification, defect routing, and test intelligence/analytics.
- Framework \& Repository Management: Integrate AI workflows with Playwright frameworks and build automated GitHub branch creation, pull request generation, and repository\-aware test generation patterns.
- CI/CD \& Quality Gates: Design and implement integrations with AWS CodeBuild and enterprise CI/CD pipelines, incorporating rule\-based quality gates and AI\-assisted failure analysis.
- Enterprise Platform Integrations: Build integrations with Databricks, Jira, qTest, and GitHub, leveraging Databricks Test Data Management (TDM) and analytics for historical context and failure classification.
- AI Governance \& Observability: Design and implement AI governance controls including prompt version control, Model Context Protocol (MCP), approved action policies, audit logging, human\-in\-the\-loop workflows, and AI action traceability.
- Cross\-Functional Collaboration: Partner with QA, DevOps, Cloud Engineering, Data Engineering, and Security teams to establish enterprise AI testing standards and platform architecture.
Required Technical Skills
- AI \& Agentic Systems: Claude, Prompt Engineering, Agentic AI Workflows, Multi\-Agent Architectures, Model Context Protocol (MCP).
- Preferred Agent Frameworks: Strands Agents, Amazon Bedrock, AgentCore.
- Programming \& Scripting: TypeScript, Node.js, JavaScript, SQL, REST API Development.
- Cloud \& AWS: AWS Services (IAM, CodeBuild, CloudWatch, S3, Secrets Manager, Parameter Store).
- DevOps \& CI/CD: GitHub, GitHub Actions, CI/CD Pipelines, Infrastructure as Code (Terraform preferred).
- Automation \& Enterprise Tools: Playwright, API Testing, Test Automation Framework Architecture, Test Data Management Concepts, Jira, qTest, Databricks.
Preferred Qualifications
- Experience: 7\+ years of software engineering, automation engineering, platform engineering, or cloud engineering experience, with 5\+ years designing and implementing enterprise test automation solutions.
- AI \& Cloud Mastery: Experience building cloud\-native solutions on AWS and integrating AI/LLM capabilities into engineering or developer automation platforms.
- Governance \& Analytics: Track record of designing governance, auditability, compliance, and operational controls, as well as working with Databricks, TDM, or analytics\-driven automation solutions.
- Location Requirements: Candidates must reside in or around the Chicago Area, Midwest, New Jersey, or New York regions.
- Education: Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent experience.
Preferred Certifications
- AWS Certified Solutions Architect
- AWS Certified AI Practitioner
- AWS Certified Developer – Associate
- HashiCorp Certified: Terraform Associate
Pay: $90,000\.00 \- $100,000\.00 per year
Work Location: Remote
Salary Context
This $90K-$100K 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 BONbLOC, 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 ($95K) sits 56% below the category median. Disclosed range: $90K to $100K.
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.
BONbLOC AI Hiring
BONbLOC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $100K - $100K.
Location Context
AI roles in Chicago pay a median of $192,900 across 197 tracked positions. That's 10% below 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 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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