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About This Role
Position Overview
=====================
Blue Acorn iCi is seeking an AI Engineer to design and ship AI\-powered solutions that solve real business problems for internal teams and enterprise customers. You will own solutions end to end, from problem framing with product and business owners, to rapid prototyping, to hardening for production.
Location: Hybrid \- Onsite 3 days a week expected in San Jose, CA
Key Responsibilities
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### AI Solution Development \& Engineering
- Build production\-grade AI solutions using LLMs, agents, RAG pipelines, and orchestration frameworks.
- Translate ambiguous business needs into clear technical designs and working software.
- Prototype quickly, then harden for scale, observability, and cost efficiency.
- Integrate AI solutions with enterprise systems including Adobe Experience Platform, Workfront, Adobe Journey Optimizer (AJO), and internal data platforms.
### AI Evaluation \& Optimization
- Define success metrics and build evaluation harnesses to measure model and system performance.
- Stay current on emerging AI capabilities and bring practical ideas back to the roadmap.
### Collaboration \& Reusable Solutions
- Partner with product managers, designers, and business stakeholders to shape requirements and demo progress.
- Contribute reusable components, internal skills, and shared tooling that lift the entire team.
Required Skills \& Experience
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- 5\+ years of software engineering experience.
- 2\+ years shipping AI or ML\-powered solutions in production.
- Strong hands\-on skills in Python and TypeScript or JavaScript.
- Comfort with React for building AI\-driven user experiences.
- Practical experience with LLM APIs such as Anthropic, OpenAI, or equivalent.
- Working knowledge of prompt engineering, tool use, structured outputs, and agent patterns.
- Experience with RAG architectures, vector databases, and retrieval quality evaluation.
- Solid grasp of cloud platforms such as AWS, Azure, or Adobe I/O Runtime.
- Familiarity with modern DevOps practices including CI/CD, logging, and monitoring.
- Clear written and verbal communication with technical and non\-technical audiences.
Preferred Qualifications/Education
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- Experience with Adobe Experience Platform, Workfront, GenStudio, or Adobe Journey Optimizer.
- Familiarity with Databricks, Snowflake, or similar data platforms.
- Background in enterprise SaaS, marketing technology, or customer engineering.
- Contributions to open\-source AI tooling or internal developer platforms.
- Experience with agent frameworks such as LangGraph, CrewAI, or similar.
Compensation
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The base salary range for this position is $125,000–$175,000 annually. Actual compensation may vary depending on experience, qualifications, geographic location, and other job\-related factors.
About Blue Acorn iCi
Blue Acorn iCi, a business unit of Infosys Nova Holdings, LLC, is a digital experience partner helping global brands design, build, and optimize the future of customer engagement. We blend data, design, and technology to deliver connected commerce and content solutions that drive measurable results.
Specializing in the Adobe Experience Cloud, we provide end\-to\-end services across commerce, content, analytics, and customer insights. Backed by Infosys, we’re growing fast—and we’re looking for exceptional talent to grow with us.
Our teams work at the intersection of strategy and execution—solving complex challenges for industry leaders in healthcare, retail, manufacturing, and beyond. From large\-scale digital transformation programs to platform implementations and ongoing optimization, we bring deep expertise and a collaborative mindset to every engagement.
Why Join Us?
Blue Acorn iCi is a place for curious thinkers, smart builders, and digital pioneers. We’re more than a consultancy—we’re a team of innovators helping the world’s top brands shape the future of digital.
Here, you won’t just contribute to a project—you’ll drive meaningful outcomes, collaborate with cross\-disciplinary teams, and grow your skills in a high\-impact, high\-growth environment. We believe great work starts with great people, and we’re committed to creating a culture where talent thrives, ideas spark change, and results speak for themselves.
If you’re ready to push boundaries, deliver real value, and help shape the next generation of digital experiences—this is your place.
*\*\*\*For US based roles, candidates must have unrestricted authorization to work in the United States without the need for employer sponsorship now or in the future. Infosys Nova Holdings, LLC does not provide immigration or visa sponsorship for this position (including H4 EAD, H‑1B, OPT, CPT, TN, O‑1, E‑3, or J‑1 visas).\*\*\**
*Full Time, Non\-Temporary Employees enjoy a competitive benefits package that includes medical, dental and vision insurance, life insurance, disability, paid time off, 401(k), and more!*
*Blue Acorn iCi is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, spouse of protected veteran, or disability.*
*Please click to read* *EEO Law* *and* *Pay Transparency Act* *and* *IER Right to Work Document* *and* *Privacy Notice**.*
*Blue Acorn iCi will endeavor to make a reasonable accommodation to the known physical or mental limitations of a qualified applicant with a disability unless the accommodation would impose an undue hardship on the operation of our business. If you believe you require such assistance to complete this form or to participate in an interview, please contact us at: [email protected].*
*California applicants: Please click* *here* *for CCPA disclosures.*
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Salary Context
This $125K-$175K 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 Blue Acorn iCi, 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 ($150K) sits 30% below the category median. Disclosed range: $125K to $175K.
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.
Blue Acorn iCi AI Hiring
Blue Acorn iCi has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, San Jose, CA, US. Compensation range: $175K - $203K.
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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