Interested in this AI/ML Engineer role at BridgeNexus Technologies Inc?
Apply Now →Skills & Technologies
About This Role
No OPT's CPT's:
1\. Automation Framework Engineering
Collaborate closely with developers to understand system architecture, data flows, and event\-driven behaviors for effective automation design.
Build and enhance end\-to\-end automation frameworks supporting API, integration, and data validation layers.
Develop integration test suites to ensure reliability and stability across development and pre\-production environments.
Automate regression, functional, and event\-driven validations using Playwright, PyTest, TestNG, and related tools.
Contribute to modernization initiatives transitioning from legacy to distributed systems, ensuring comprehensive automation coverage and system resilience.
2\. AI \& Intelligent Test Enablement
Leverage tools such as GitHub Copilot, ChatGPT, and LLM\-based agents to assist in automation scripting, test generation, and analysis.
Integrate GenAI tools for test documentation, failure triage, and predictive quality analytics.
Experiment with autonomous testing agents capable of self\-triggering and monitoring test pipelines.
Drive the adoption of AI\-enhanced automation strategies to improve efficiency, quality insights, and test coverage.
3\. Cloud \& DevOps Integration
Integrate automation frameworks within CI/CD pipelines (GitHub Actions, Jenkins, TeamCity) to enable continuous validation.
Deploy and manage test environments on AWS (EKS, SQS, Glue, Lambda, DynamoDB, PostgreSQL).
Implement observability and test telemetry to monitor pipeline health, performance, and coverage trends.
Collaborate with DevOps and platform engineering teams to ensure environment consistency, scalability, and release readiness.
4\. Agile Collaboration \& Leadership
Actively participate in Agile/Scrum ceremonies—including sprint planning, daily standups, and retrospectives—to align testing and development objectives.
Mentor engineers in automation best practices and modern framework adoption.
Foster cross\-functional alignment by collaborating with developers, architects, and product owners to ensure quality is engineered from the start.
Champion a quality\-first culture by embedding continuous testing and automation throughout the delivery lifecycle.
Tech Stack \& Tools
Languages: Java/C\#/ Python, JavaScript/TypeScript
Frameworks: Playwright, Selenium, PyTest, TestNG, JUnit
Cloud \& DevOps: AWS (EKS, Glue, SQS, Lambda, DynamoDB), Jenkins, GitHub Actions, Docker, Kubernetes
Databases: PostgreSQL, Redis, DynamoDB, MongoDB
AI Tools: GitHub Copilot, ChatGPT, OpenAI Assistants
Methodologies: Agile, Scrum, Domain\-Driven Design, Event\-Driven Architecture
Required Skills \& Experience
Experience in test automation and quality engineering within complex enterprise environments.
Strong understanding of software development workflows with proven collaboration alongside development and architecture teams.
Expertise in Playwright and API automation frameworks, with proficiency in Java, Python, or C\#.
Experience testing event\-driven systems and microservice\-based architectures.
Hands\-on experience with AWS cloud services and CI/CD automation practices.
Exposure to GenAI tools (Copilot, ChatGPT, LLM\-based assistants) for enhanced automation productivity.
Proven experience working in Agile/Scrum environments as an active and contributing team member.
Preferred Qualifications
Knowledge of AI\-based automation, autonomous test agents, or intelligent QA platforms.
Certifications in AWS DevOps, Google Cloud, or Azure Fundamentals.
Experience with financial systems or data\-intensive platforms.
Strong leadership and communication skills, with a passion for mentoring and cross\-functional collaboration.
Pay: $129,939\.02 \- $140,399\.67 per year
Benefits:
- Health insurance
Work Location: Hybrid remote in Austin, TX 78789
Salary Context
This $129K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At BridgeNexus Technologies 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($135K) sits 24% below the category median. Disclosed range: $129K to $140K.
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 ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
BridgeNexus Technologies Inc AI Hiring
BridgeNexus Technologies Inc has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Austin, IN, US, Austin, TX, US. Compensation range: $124K - $140K.
Location Context
AI roles in Austin pay a median of $218,800 across 493 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 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 ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.