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About This Role
Benefits:
- 401(k)
About the Role
We are seeking an experienced AI Consultant / AI Solutions Engineer to design, develop, and deploy AI\-powered solutions that solve complex business problems.
The ideal candidate will have strong hands\-on experience building production\-grade AI applications using LLMs, Generative AI, AI agents, RAG architectures, and modern orchestration frameworks.
This role requires someone who can take ownership of AI solutions end\-to\-end—from understanding business requirements and rapidly prototyping solutions to building scalable, observable, and production\-ready applications.
Key Responsibilities
- Design and build production\-grade AI solutions using LLMs, AI agents, RAG pipelines, and orchestration frameworks.
- Translate ambiguous business requirements into clear technical designs and working solutions.
- Rapidly prototype AI solutions and harden them for production scalability, reliability, observability, and cost efficiency.
- Develop evaluation frameworks and success metrics to measure AI model and application performance.
- Integrate AI solutions with enterprise platforms and systems, including Adobe Experience Platform, Workfront, Adobe Journey Optimizer (AJO), and internal data platforms.
- Work closely with Product Managers, UX/UI teams, engineers, and business stakeholders to define requirements and demonstrate progress.
- Develop reusable AI components, tools, frameworks, and internal capabilities.
- Evaluate emerging AI technologies and identify opportunities to improve products and business processes.
- Contribute to architecture discussions, technical documentation, testing, monitoring, and production support.
- Collaborate with engineering and DevOps teams to implement CI/CD, logging, monitoring, and operational best practices.
Required Qualifications
- 5\+ years of software engineering experience.
- 2\+ years of hands\-on experience delivering AI/ML\-powered solutions to production.
- Strong hands\-on programming experience with Python.
- Strong experience with TypeScript and/or JavaScript.
- Experience building AI\-powered user experiences using React.
- Hands\-on experience with LLM APIs such as OpenAI, Anthropic, or equivalent platforms.
- Strong understanding of:
+ Prompt Engineering
+ Tool Calling
+ Structured Outputs
+ AI Agent Patterns
+ LLM Application Development
- Experience designing and implementing RAG architectures.
- Experience with vector databases and retrieval optimization/evaluation.
- Understanding of cloud platforms such as AWS, Azure, or Adobe I/O Runtime.
- Familiarity with modern CI/CD, logging, monitoring, testing, and DevOps practices.
- Strong communication skills with the ability to work with both technical and non\-technical stakeholders.
- Strong analytical and problem\-solving skills.
Preferred Qualifications
- Experience with Adobe Experience Platform (AEP).
- Experience with Adobe Workfront, Adobe GenStudio, or Adobe Journey Optimizer (AJO).
- Familiarity with Databricks, Snowflake, or similar enterprise data platforms.
- Experience in enterprise SaaS, marketing technology, customer engineering, or digital experience platforms.
- Experience contributing to open\-source AI tooling or internal developer platforms.
- Experience with AI agent frameworks such as LangGraph, CrewAI, or similar frameworks.
- Experience designing enterprise\-grade AI architectures and scalable GenAI solutions.
- Experience working in Agile/Scrum environments.
Ideal Candidate
The ideal candidate is a hands\-on AI engineer/consultant who can bridge business requirements, AI architecture, software engineering, and production delivery.
They should have demonstrated experience taking AI solutions from concept and prototype through production while understanding scalability, security, observability, cost optimization, and enterprise integration requirements.
Important Location Requirement
This position is open to candidates who are local to San Jose, CA. Candidates must be able to work from the San Jose area.
This is a remote position.
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 4,317 AI roles we're tracking, AI Consultant positions make up 0% of the market. At UNICON SYSTEMS, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Consultant roles pay a median of $145,144 based on 14 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($135K) sits 7% below the category median. Disclosed range: $128K to $141K.
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.
UNICON SYSTEMS AI Hiring
UNICON SYSTEMS has 1 open AI role right now. They're hiring across AI Consultant. Based in US. Compensation range: $141K - $141K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
Career Path
Common paths into AI Consultant roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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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