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About This Role
Position Overview
We are seeking a Staff IT AI Implementation Engineer to lead the architecture, deployment, and operational oversight of our enterprise AI ecosystem. Sitting at the intersection of hands-on technical execution and strategic governance, this role is pivotal in building production-ready AI solutions, automating cross-functional workflows, and shaping responsible AI adoption standards across the organization.
The ideal candidate thrives on delivering scalable AI integrations while acting as a trusted advisor to help business units understand, deploy, and maximize the value of artificial intelligence securely.
Core Responsibilities
- AI Platform Administration: Manage enterprise AI platforms (including frontier LLMs like Claude Enterprise and ChatGPT Enterprise), ensuring secure access controls and seamless coordination with IT infrastructure.
- Workflow Automation & Agent Development: Design, build, deploy, and maintain custom AI agents and automated workflows to solve business challenges while adhering to IT security standards.
- Cross-Functional Advisory: Serve as the primary point of contact for AI requests across all business units. Partner with stakeholders to assess feasibility, scope technical requirements, and deliver production-ready implementations.
- Governance & Security Compliance: Collaborate with IT Security and leadership to establish, implement, and enforce AI governance frameworks and acceptable use policies.
- Vendor & Tool Evaluation: Review and vet emerging AI tools, services, and platform releases alongside IT Security to provide recommendations for adoption or integration.
- Enablement & Training: Develop cross-departmental guidance, best practices, and training programs to support safe, compliant, and effective AI usage.
- Performance & Cost Optimization: Track platform usage metrics, monitor cost allocations, and present operational insights and optimization strategies to leadership.
- Security Reviews: Ensure all agents, MCP servers, integrations, and data connections undergo rigorous IT Security review before deployment, upholding principles of least-privilege access.
Key Qualifications
- Experience: 3+ years of experience in software engineering, data science, ML engineering, or applied AI (or 2+ years specialized in building production LLM applications).
- AI Model Integration: 1–3 years of hands-on experience working with frontier AI services, LLMs, or agent development.
- Agent Architecture: Proven ability to build AI agents utilizing tool/function calling, API integrations, and Model Context Protocol (MCP) frameworks.
- RAG Pipelines: Technical proficiency in Retrieval-Augmented Generation (RAG) architecture, including embeddings, vector databases, chunking strategies, and retrieval evaluation.
- LLM Evaluation: Hands-on experience evaluating LLM performance using test sets, LLM-as-judge frameworks, and prompt regression testing.
- Prompt & Context Engineering: Advanced expertise in production-grade prompt engineering and context optimization.
- AI Security Standards: Strong knowledge of AI-specific security threats (e.g., prompt injection, data leakage, PII handling) and familiarity with the OWASP Top 10 for LLMs.
- Deployment & Managed Services: Experience containerizing AI applications, managing self-hosted/served models, and utilizing cloud AI services (e.g., AWS Bedrock, Azure OpenAI/AI Foundry, Google Vertex AI).
- Governance Frameworks: Familiarity with responsible-AI practices and governance models (such as NIST AI RMF or ISO/IEC 42001).
- Communication & Collaboration: Demonstrated track record of collaborating across business units and translating complex technical concepts for non-technical stakeholders.
Preferred Qualifications
- Certifications:
Anthropic Claude Certified Associate / Developer
+ AWS Certified AI Practitioner or AWS Certified Generative AI Developer + Microsoft Azure AI Fundamentals + CompTIA AI+ or equivalent vendor-neutral credential +
- Frameworks & Orchestration: Experience with agent orchestration tools (e.g., LangChain, LangGraph, AutoGen).
- Observability: Experience using LLM observability and cost-tracking platforms (e.g., LangSmith).
- Automation Platforms: Familiarity with workflow automation tools (e.g., Power Automate, n8n, Zapier).
- Model Customization: Hands-on experience with fine-tuning or local deployment frameworks (e.g., Ollama, OpenWebUI).
Company Culture & Benefits
We foster a collaborative, innovative culture centered on continuous growth, cross-functional mentorship, and transformative technology.
We offer a comprehensive benefits package designed to support full employee well-being, including:
Competitive medical, dental, and vision health coverage
* Retirement savings plans with employer matching * Short-term and long-term disability protection * Paid time off, holidays, and parental leave programs *
- Wellness programs and dedicated professional development initiatives
Salary Context
This $150K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At AVISO, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($160K) sits 25% below the category median. Disclosed range: $150K to $170K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
AVISO AI Hiring
AVISO has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fremont, CA, US. Compensation range: $170K - $170K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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