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About This Role
Role 1: AI Analyst with Healthcare Background
Location: Woodland Hills. CA (Commute three times in a week to Woodland Hills office)
Duration: Long term contract
Min experience: 10 \+years
Client: Direct client
Role 2: AI ML Developer with Data Science experience
Location: Woodland Hills. CA (Commute three times in a week to Woodland Hills office) Duration: Long term
Min experience: 10 \+years
Client: Direct client
\#Roles Job Description
Role 1\) AI Analyst with Healthcare Background
seeking an experienced Artificial Intelligence \& Machine Learning (AI/ML) Systems Analyst to bridge the gap between business stakeholders, data scientists, AI/ML engineers, and technology teams. This role will be responsible for analyzing business problems, defining AI/ML solution requirements, evaluating data readiness, supporting model implementation, and ensuring AI solutions align with business objectives, regulatory requirements, and enterprise governance standards. The ideal candidate will possess strong analytical skills, healthcare domain knowledge, and experience working with AI, machine learning, and Generative AI technologies.
Key Responsibilities Business \& Systems Analysis
- Collaborate with business stakeholders to identify opportunities for AI/ML\-driven process improvements and automation.
- Elicit, analyze, and document business, functional, and non\-functional requirements.
- Create user stories, process flows, use cases, data mappings, and acceptance criteria.
- Translate business requirements into AI/ML solution specifications.
- Support backlog grooming, sprint planning, and Agile delivery processes.
AI/ML Solution Analysis
- Partner with AI/ML engineers and data scientists to define model objectives and success metrics.
- Analyze data sources and determine data quality, completeness, and readiness for AI initiatives.
- Support predictive analytics, recommendation systems, NLP, GenAI, and intelligent automation initiatives.
- Evaluate AI model outputs and assist with validation, testing, explainability, and business adoption.
Data \& Analytics
- Perform data analysis using SQL, Python, Power BI, and reporting tools.
- Develop dashboards and reporting mechanisms to monitor AI model performance and business outcomes.
- Support data governance, lineage, quality, and compliance activities.
- Assist in feature identification and business rule definition for ML models.
AI Governance \& Compliance
- Ensure AI solutions comply with enterprise AI policies, security standards, and regulatory requirements.
- Support documentation of model governance, auditability, explainability, and risk assessments.
- Participate in AI solution reviews, testing, and deployment readiness activities.
Stakeholder Engagement
- Act as a liaison between business teams, product owners, architects, data engineers, and AI/ML engineers.
- Communicate findings, recommendations, and project status to leadership and project stakeholders.
- Facilitate workshops, requirement sessions, and solution reviews.
Required Qualifications Education
- Bachelor's degree in Computer Science, Information Systems, Data Science, Artificial Intelligence, Engineering, Mathematics, or a related field.
- Master's degree preferred.
Experience
- 5\+ years of Systems Analysis, Business Analysis, Product Analysis, or related experience.
- 2\+ years supporting AI/ML, Advanced Analytics, Data Science, or Generative AI initiatives.
- Experience working within Agile/Scrum delivery environments.
- Healthcare, insurance, or regulated industry experience preferred.
Technical Skills
- SQL and relational databases
- Python or R for analytics
- Power BI, Tableau, or equivalent reporting tools
- Machine Learning concepts and model lifecycle understanding
- Generative AI, LLMs, Retrieval\-Augmented Generation (RAG), and prompt engineering concepts
- Cloud platforms such as Azure, AWS, or GCP
- Azure AI Services, Azure Machine Learning, or equivalent AI platforms preferred
Preferred Skills
- Healthcare benefits, claims, member, provider, or utilization management domain knowledge.
- Experience with AI governance frameworks and responsible AI practices.
- Understanding of MLOps concepts, model monitoring, and deployment processes.
- Knowledge of NLP, Computer Vision, Predictive Analytics, and Agentic AI architectures.
- Familiarity with Jira, Azure DevOps, Confluence, and Git\-based development workflows.
Role 2\) AI ML Developer with Data Science experience
- Hands\-on experience with Agentic Layer A2A frameworks and MCP Protocol.
- Expertise in AI/ML engineering, specifically vector embeddings, prompt engineering, and context engineering.
- Strong programming skills in at least two of the following: Python, Java, Go.
- Proficiency in deploying solutions on Azure Cloud.
- Experience with databases such as Azure AI Search, Redis, and Cosmos DB (Blob Storage and Iceberg are plus).
- Proven ability to design and manage Azure Functions and Azure Container Apps.
- Strong understanding of cloud\-native architecture, scalability, and performance optimization.
- Strong Communication skills.
Thanks \& Regards\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Sandy
Email: [email protected]
Desk: 510\-400\-7029 Talent Acquisition Specialist
BRIDGENEXUS TECHNOLOGIES INC
41829 Albrae Street, Suite 112
Fremont, California 94538
Pay: $40\.00 \- $120\.00 per hour
Benefits:
- 401(k)
Application Question(s):
- Do have more than 12\+ years of experience AND in that do you have at\-least 4\-5\+ years working experience on AI ML? (yes or no)
- This position is hybrid and requires 3 days per week onsite at Woodland Hills, CA and Are you: Local to Los Angeles or Woodland Hills, CA or willing to relocate woodland hills CA? (yes or no)
Willing to relocate to Woodland Hills, CA
- As per client requirement this position is only open to Citizens. Are you a Citizen? (yes or no)
Work Location: In person
Salary Context
This $83K-$249K range is below 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 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($166K) sits 23% below the category median. Disclosed range: $83K to $249K.
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.
BridgeNexus Technologies Inc AI Hiring
BridgeNexus Technologies Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Woodland Hills, CA, US. Compensation range: $249K - $249K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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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