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About This Role
Job Description:
Role Purpose
We are seeking a highly experienced AI Evangelist & Core AI Engineer who combines deep technical expertise in Artificial Intelligence with the ability to influence, educate, demonstrate, and accelerate AI adoption across the enterprise.
This is not primarily an advisory role. The successful candidate will be expected to design, build, demonstrate, deploy, and scale real AI solutions, while simultaneously acting as an internal AI champion who helps engineering teams, business leaders, architects, and delivery organizations understand how emerging AI technologies can create measurable business value.
The role requires deep understanding of AI, Machine Learning, Deep Learning, Generative AI, Large Language Models, Agentic AI, AI engineering, and modern software development practices, combined with significant hands-on experience implementing scalable AI applications in complex enterprise environments.
The individual should be comfortable moving from a business problem to architecture, prototype, production implementation, enterprise standards, reusable reference patterns, and organization-wide adoption.
Key Responsibilities
1. Core AI Engineering & Architecture
- Design, develop, and implement enterprise-grade AI and Generative AI applications.
- Build scalable applications using LLMs, multimodal models, AI agents, RAG, vector databases, knowledge graphs, APIs, and enterprise data platforms.
- Architect AI solutions capable of supporting enterprise-scale workloads with appropriate considerations for: + Scalability + Reliability + Security + Performance + Observability + Cost optimization + Governance
- Develop and implement advanced Retrieval-Augmented Generation (RAG) architectures including hybrid search, reranking, metadata filtering, semantic retrieval, and knowledge grounding.
- Build multi-agent and agentic AI applications capable of reasoning, tool usage, workflow execution, orchestration, and enterprise system integration.
- Design AI applications integrating structured and unstructured enterprise information.
- Evaluate architecture choices between commercial models, open-source models, small language models, domain-specific models, and hosted AI platforms.
- Implement model routing, prompt orchestration, caching, guardrails, evaluation pipelines, and fallback strategies.
- Work closely with cloud, platform, security, enterprise architecture, DevOps, and application engineering teams.
2. Deep AI / ML / DL Expertise
The candidate should have strong conceptual and practical understanding across the modern AI technology landscape, including:
- Artificial Intelligence /Machine Learning/ Deep Learning/ Neural Networks /LLM/SLM/RAG/Graph RAG / MLOps / LLMOps / GenAIOps
3. Generative AI & LLM Engineering
- Build production-grade applications utilizing leading commercial and open-source LLM ecosystems.
- Design scalable LLM platforms supporting multiple enterprise applications and use cases.
- Develop reusable LLM services, APIs, components, agent frameworks, and reference architectures.
4. AI Agents & Automation
- Design and implement AI agents and agentic workflows that automate complex business and engineering processes.
- Build agents capable of interacting with enterprise applications, APIs, databases, documents, development environments, and workflow platforms.
- Evaluate emerging agent architectures and orchestration frameworks pragmatically.
- Develop reusable patterns for: + Single-agent systems + Multi-agent systems + Human-in-the-loop workflows + Autonomous workflows + Tool-calling agents + Coding agents + Enterprise knowledge agents
- Establish appropriate controls for AI agent identity, permissions, auditability, security, and human oversight.
5. AI-Enabled Software Engineering
Drive adoption of AI throughout the Software Development Life Cycle.
6. Build Frontier AI Demonstrations
A critical part of the role is demonstrating what is technically possible.
The individual will:
- Build advanced AI prototypes and demonstrations before formal enterprise implementation.
- Experiment with emerging AI models, agents, frameworks, development tools, and architectures.
- Convert emerging technology into demonstrable business scenarios.
- Build solutions directly using APIs, SDKs, terminals, development environments, cloud platforms, and enterprise applications.
- Create demonstrations that explain both: + What technology can reliably achieve today + What remains experimental or emerging
- Identify limitations and edge cases before solutions reach production.
- Feed technical findings back into enterprise architecture, product strategy, AI standards, and engineering practices.
7. AI Evangelism & Enterprise Adoption
Act as a visible internal champion for responsible and effective AI adoption.
Responsibilities include:
- Educate engineering and business teams about practical AI opportunities.
- Demonstrate emerging AI
Department: ETI
Open Positions: 1
Skills Required: Machine Learning,Artificial Intelligence Engineer,Deep Learning,Artificial Intelligence,Azure Cognitive Services,Ai Solutions,Ai Platform,Ai Techniques,Artificial Intelligence Developer
Role:
Role Purpose
We are seeking a highly experienced AI Evangelist & Core AI Engineer who combines deep technical expertise in Artificial Intelligence with the ability to influence, educate, demonstrate, and accelerate AI adoption across the enterprise.
This is not primarily an advisory role. The successful candidate will be expected to design, build, demonstrate, deploy, and scale real AI solutions, while simultaneously acting as an internal AI champion who helps engineering teams, business leaders, architects, and delivery organizations understand how emerging AI technologies can create measurable business value.
The role requires deep understanding of AI, Machine Learning, Deep Learning, Generative AI, Large Language Models, Agentic AI, AI engineering, and modern software development practices, combined with significant hands-on experience implementing scalable AI applications in complex enterprise environments.
The individual should be comfortable moving from a business problem to architecture, prototype, production implementation, enterprise standards, reusable reference patterns, and organization-wide adoption.
Location: FBD
Education/Qualification: BE/BTech
Years Of Exp: Above 8 years
Salary: 4000000 to 5000000
Posted On: 17-Sep-2026
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Yamaha Motor, 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 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Yamaha Motor AI Hiring
Yamaha Motor has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Antonio, TX, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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