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About This Role
AI Engineer
What You'd Be Doing
- Drive an AI\-first culture through internal playbooks and "golden\-path" templates while measuring impact via DORA and SPACE metrics.
- Manage AI costs through token budgeting and usage tracking alongside guardrails like PII redaction and audit logging.
- Build and document reusable patterns for code generation, PRs, testing, and debugging to optimize the end\-to\-end developer lifecycle.
- Conduct POCs and provide recommendations for AI tools based on ROI, technical merit, and stakeholder feedback.
- Manage lightweight AWS infrastructure including API Gateways and LLM pipelines while integrating tools with CI/CD and GitLab.
What We Need to See
- 8\+ years in platform engineering, DevOps, developer experience, or a closely related technical discipline.
- Demonstrated hands\-on experience with LLM APIs and AI developer tooling in production or organizational contexts
- Experience evaluating, procuring, or governing AI/SaaS tools at an organizational level, including vendor assessment, license management, and cost governance.
- Strong Python skills for automation, tooling, and lightweight AI workflow and integration development.
- Practical, daily use of AI\-assisted development tools (GitHub Copilot, Cursor, Claude Code, ChatGPT, or similar) in your own engineering workflows.
- Experience designing developer workflows, internal platforms, or engineering self\-service capabilities with a focus on adoption and usability.
- Solid AWS experience with familiarity with Bedrock, API Gateway, or equivalent managed AI and cloud services.
- Strong observability mindset with the ability to instrument AI tooling and workflows with meaningful metrics and usage signals.
- Infrastructure\-as\-code familiarity (Terraform, Helm) and experience working within GitOps and CI/CD environments, with GitLab CI preferred.
- Excellent communication and stakeholder management skills, with the ability to translate technical findings into clear recommendations for engineering leadership and business audiences.
Ways to Stand Out from the Crowd
- Experience building or contributing to an internal AI enablement function, center of excellence, or developer experience program.
- Hands\-on experience with LLM Agents, RAG pipelines, vector databases (pgvector, OpenSearch, Pinecone, or similar), and prompt orchestration frameworks such as LangChain or LlamaIndex.
- Familiarity with AI FinOps tooling, cost attribution models, and LLM API usage reporting at an organizational scale.
- Experience with AI governance frameworks including acceptable use policies, audit logging, PII redaction pipelines, and responsible AI practices in regulated enterprise environments.
- Background in financial services or insurance with an understanding of compliance constraints on AI tool usage and data handling.
- Experience with AI\-specific security threat models including OWASP Top 10 for LLMs, prompt injection risks, and model supply chain security.
- Familiarity with developer productivity metrics frameworks such as DORA or SPACE, and a track record of using data to demonstrate engineering impact.
- Strong ownership demeanor with a structured, automation\-first approach and demonstrated impact driving AI\-first engineering practices across teams.
ABOUT ISHIR
ISHIR is a digital innovation and enterprise AI services provider. We work with startups and enterprises to shape the future through accelerated innovation, deep technical expertise, access to global digital talent and a passion for complex problem\-solving. With our help, our clients overcome their most difficult digital challenges leveraging AI.
We are not just consultants, we are partners in our clients’ success, assisting them with re(gaining) competitive edge by identifying opportunities for differentiation, industry disruption, scalable innovation, and go\-to\-market strategies that deliver successful outcomes.
At ISHIR, we help bold businesses accelerate innovation through Talent, Speed\-to\-Market, and AI. We help make an impact by solving real problems using innovation, improved customer experiences and the right technologies.
As an ISHIR employee, you will get the advanced training you need to be successful, and the opportunity to apply it. You must be passionate about technology, crave responsibility, and be eager to apply your knowledge to real business solutions for our startup and enterprise customers. These are the qualities of a person destined for success at ISHIR.
ISHIR attracts a special type of individual—someone who is proactive, thrives on challenges, feeds off success, and looks at moving targets not as obstacles but as opportunities. ISHIR is an exciting place to work. It is imbued with an entrepreneurial spirit and promotes self\-reliance, open communication, and collaboration.
Pay: $80,000\.00 \- $100,000\.00 per hour
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At ISHIR, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
ISHIR AI Hiring
ISHIR has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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