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About This Role
About the company
Xen.AI, is an Artificial Intelligence (AI) research and development (R&D) organization with operations in USA and India. We help our customers to develop innovative and custom solutions using Artificial Intelligence, Machine Learning, Deep Learning, Data Science and Open Source technologies. Please visit https://xen.ai/ to know more about us.
Overview
We are seeking a dynamic, hands-on Director of Engineering / Lead Full Stack Azure Developer to spearhead the design, development, and deployment of cutting-edge AI solutions within the healthcare and BFSI (Banking, Financial Services, and Insurance) sectors. This role offers an exciting opportunity to lead cross-functional teams in building scalable, secure, and innovative enterprise-grade applications leveraging cloud infrastructure and advanced AI technologies. The ideal candidate will be a strategic thinker with deep technical expertise who thrives in fast-paced environments and is passionate about leveraging technology to solve complex industry challenges.
Compensation for this position
- Please note - For the 1st year - 100% revenue based compensation based on the client projects that you help to get and deliver.
- After 1st year, based on company's progress we can consider formal corporate positions with market standard salary and equity etc.
Duties
- Lead the architecture, design, and implementation of full-stack applications utilizing Azure cloud services, ensuring robust security and compliance standards are met.
- Drive the development of AI-powered healthcare and BFSI solutions by integrating machine learning models, data analytics, and automation tools.
- Manage end-to-end software development lifecycle (SDLC), including planning, coding, testing, deployment, and maintenance of enterprise applications.
- Collaborate with product managers, data scientists, security teams, and stakeholders to define technical requirements and translate them into scalable solutions.
- Promote a culture of continuous improvement through Agile methodologies like Scrum or Kanban; foster innovation through DevOps practices such as CI/CD pipelines.
- Manage technical architecture decisions involving APIs, databases (SQL Server) and cloud infrastructure (Azure).
- Provide executive leadership by translating complex technical concepts into clear business strategies; manage budgets and resources effectively for maximum impact.
Experience
- 10+ years of proven hands-on, application development experience using Azure cloud services and infrastructure.
- Hands-on development experience with Azure cloud services.
- Programming experience: Python, SQL, Azure cloud services.
- Experience developing Machine Learning based products.
- Experience with MLOps and DevOps practices preferred.
- Experience with VSCode, Github is required.
- Proven track record of successfully delivering innovative software and/or AI/ML.
- Highly self motivated.
- Well organized, focused, aggressive, result oriented, can-do attitude.
- Excellent leadership skills.
- Excellent communication skills.
- Excellent team management and leadership experience.
- Bachelors/Masters Degree in Computer Science or related field.
- Deep domain knowledge in Healthcare or Banking, Financial Services, and Insurance (BFSI) sectors preferred.
Must have Work Authorization in the US, we will not be able to support any VISA sponsorship. No H1B or OPT.
Join us to lead innovative AI-driven solutions that redefine healthcare and financial services! This role offers an inspiring environment where your expertise will shape the future of enterprise technology—empowering organizations through strategic innovation and technological excellence.
Pay: $1.00 per year
Benefits:
- Flexible schedule
Work Location: Remote
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 9,253 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Xen.ai, 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 $212,000 based on 13,052 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,382; Director: $272,300; VP: $235,830.
Xen.ai AI Hiring
Xen.ai has 3 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 9,253 open positions tracked in our dataset. By seniority: 107 entry-level, 4,462 mid-level, 3,410 senior, and 1,274 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (530 positions). The remaining 8,653 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,253 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,989), AI Software Engineer (462), AI Product Manager (457). 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 (107) are outnumbered by mid-level (4,462) and senior (3,410) 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 1,274 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (530 positions), with 8,653 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 $213,000. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,638 postings), Aws (893 postings), Azure (718 postings), Rag (666 postings), Gcp (532 postings), Pytorch (434 postings), Claude (408 postings), Kubernetes (402 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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