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About This Role
One of our clients is seeking an experienced Lead AI Engineer, with expertise in context engineering and agent design who will develop AI solutions in Google, AWS and Microsoft ecosystems and will have the opportunity to mentor and lead AI engineers in the team.
Lead AI Engineer
Remote
Interview \- Teams
Long Term
Role Summary
- JFS is seeking an experienced Lead AI Engineer, with expertise in context engineering and agent design who will develop AI solutions in Google, AWS and Microsoft ecosystems and will have the opportunity to mentor and lead AI engineers in the team. Responsibilities include building and (/or) mentoring AI engineers to build conversational agents, integrating with natural language processing or generative AI, and agents/ virtual assistants to software applications. AI Engineer is responsible for designing and delivering data access through APIs and ensuring that these solutions are securely deployed.
Core Responsibilities
- Developing applications that interact with web services, APIs, and other software applications.
- Mentoring and leading AI design and development efforts as needed.
- Conducting peer reviews for AI design and development efforts to ensure existing standards are adhered to and establish additional standards as needed.
- Designing the overall structure of Agentic AI solutions, including determining necessary features, user flow, API development and integration with other systems. Requires understanding of API design principles and specific constraints of AI service interfaces.
- Implementing and tuning Natural Language Processing techniques to improve AI assistants’ understanding and response accuracy.
- Training and deploying AI models, and monitoring solution performance by creating and utilizing Dashboards.
- Creating custom indexes, managing data sources, and tuning intent matches to improve accuracy and relevance of results within the AI solution context.
- Designing conversational flows that provide a seamless and intuitive user experience, with strong focus on making the agent interactions as human\-like and engaging as possible.
- Analyzing agent interaction data to identify patterns, trends, and areas for improvement. Preparing reports on agent performance metrics.
- Identifying and resolving issues related to agent performance, including debugging and updating the system as necessary.
- Ensuring the AI agent/ conversational assistant complies with data privacy laws and maintaining high standards of security, especially when handling sensitive user data.
- Working effectively with cross\-functional teams, including business analysts, UX designers, project managers, and business customers and business SMEs from across the enterprise. Good communication skills are essential.
- Collaborating effectively with Automation Center of Excellence (CoE) team members, incorporating the established standards; adhering to organization process and ensuring that solutions align with target architectures and technology roadmaps.
- Ensuring that AI systems operate safely, transparently and within acceptable boundaries in alignment with the organization’s governance policies and practices.
- Planning, initiating and spearheading proof of concept (POC) initiatives using emerging technologies.
- Technical writing leveraging tools such as Word, PowerPoint, Visio, SharePoint, and Excel to develop presentations and white papers.
Skills \& Credentials
- Extensive knowledge and experience developing AI Conversation agents using Google tools such as CX Agent Studio, Google Conversational Agents, Google Dialogflow CX, and(/or) Google CCAI Services is required.
- Experience in creating and maintaining Enterprise Looker Dashboards for monitoring performance of implemented AI Agents is required.
- Experience developing Generative AI solutions in AWS ecosystem including but not limited to AWS services such as AWS Bedrock, AWS Transcribe, AWS Comprehend, Lamda and Step Functions is required.
- Ability to develop AI agents/ virtual assistants using Microsoft Foundry with Azure services like Azure AI Search, and Azure AI Language Service, Microsoft Power Platform and Microsoft CoPilot Studio.
- Ability to integrate virtual assistants with various Microsoft services (such as Office 365, SharePoint) and third\-party APIs to enhance functionality.
- Deep understanding of API design principles and specific constraints of AI service interfaces.
- Strong programming skills in languages such as C\#, .NET, JavaScript, or Python. Experience in developing, testing, and deploying chatbot applications.
- Ability to facilitate and lead discussions with business SME’s.
- Ability to create and present Ai solution proposals and communicate both business and technical concepts.
- Ability to lead and conduct emerging technology analysis and conduct proof of concept initiatives.
- Experience in training and deploying AI models.
- Experience in leading efforts to create technology solutions and architectures impacting critical areas of the business.
- Ability to establish and maintain a high\-level of customer trust and confidence.
- Ability to think critically and solve problems.
- Strong consultative skills at a cross\-functional level.
Education \& Experience
- Bachelor’s degree in computer science, Information systems or related discipline, or equivalent and extensive related project experience; Master’s degree preferred.
- Six years of experience in IT, with at least 4 years AI Conversational Agents and (or) Generative AI combined implementation experience using Microsoft, Google and AWS ecosystems.
- 2 years’ AI Engineer Experience developing Google conversational agents / CX Agent Studio / Dialogflow CX Web and Voice Virtual Assistants is mandatory.
- 2 years developing Enterprise Looker Dashboards to monitor AI Agent performance is mandatory.
- 2 years’ AI Engineer Experience in AWS ecosystems using AWS services such as AWS Bedrock, AWS Transcribe, AWS Comprehend, Lamda and Step Functions are mandatory.
- Agentic AI Developer Experience using Microsoft Foundry is preferred.
- Experience developing and maintaining SharePoint online solutions using MS Power Platform preferred.
- Google and AWS Certifications relevant to this role preferred.
- Minimum of six years of hands\-on design and implementation experience in IT, with knowledge in a minimum of two of the following technical disciplines:
- Application development
- Network design
- Middleware
- Servers and storage
- Database management
- Operations
- Artificial intelligence and machine learning fundamentals
- Natural language processing (NLP) and large language model (LLM) integration
Required Skills:
- Bachelor’s degree in computer science, Information systems or related discipline, or equivalent and extensive related project experience
- Six years of experience in IT, with at least 4 years AI Conversational Agents and (or) Generative AI combined implementation experience — 6 Years
- AI Engineer Experience developing Google conversational agents / CX Agent Studio / Dialogflow CX Web and Voice Virtual Assistants — 2 Years
- 2 years developing Enterprise Looker Dashboards to monitor AI Agent performance — 2 Years
- AI Engineer Experience in AWS ecosystems using AWS services such as AWS Bedrock, AWS Transcribe, AWS Comprehend, Lamda and Step Functions — 2 Years
Highly Desired
- Google and AWS Certifications relevant to this role preferred.
Salary Context
This $156K-$166K 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 eBusiness Solutions Inc., 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($161K) sits 25% below the category median. Disclosed range: $156K to $166K.
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.
eBusiness Solutions Inc. AI Hiring
eBusiness Solutions Inc. has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Columbus, OH, US. Compensation range: $166K - $199K.
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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