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About This Role
Role: Sr AI Engineering Manager\_Machine Learning
Experience: \- Minimum 10\+ Years
Location: \- USA Remote
Hiring Type: \- C2C Visa Independent
We are looking for a AI Engineer to design, build, and deploy high\-quality AI\-powered features with end\-to\-end ownership from prototyping to production, ensuring reliable, scalable, and impactful AI solutions.
Responsibilities: \-
End\-to\-End AI Feature Ownership
- Design and implement AI\-powered features (LLM workflows, copilots, and agent\-based systems with tool use and multi\-step reasoning)
- Own the Full Lifecycle: prototyping evaluation production deployment iteration
- Ensure solutions are reliable, performant, and aligned with product needs
AI System Implementation
- Build and optimize prompt pipelines for specific use cases
- Build retrieval systems (embeddings, chunking, ranking)
- Implement RAG\-based workflows where needed
- Iterate on outputs to improve quality, accuracy, and consistency
- Design scalable and cost\-efficient AI architectures for production workloads
- Select and evaluate models (hosted vs open\-source) based on use case constraints
Agent\-Based Systems (AgentCore)
- Design and build agentic workflows capable of multi\-step reasoning and decision\-making
- Integrate agents with tools, APIs, and internal systems to perform real\-world actions
- Implement planning, execution, and reflection loops for complex tasks
- Manage context, memory, and state across multi\-step interactions
- Balance deterministic workflows vs. agent autonomy for reliability and control
Experimentation \& Evaluation
- Run structured experiments to compare approaches (prompting, retrieval, models)
- Define and track key metrics for AI performance (quality, latency, cost)
- Debug and improve non\-deterministic system behavior
- Build and maintain evaluation datasets and benchmarks
- Implement automated evaluation pipelines for continuous improvement
Collaboration \& Contribution
- Drive technical direction and influence AI adoption across teams
- Partner with product managers and designers to scope AI features
- Contribute to shared patterns and reusable components
- Participate in code reviews and design discussions
- Support and mentor mid\-level engineers where needed
AI Reliability, Safety \& Governance
- Design guardrails to ensure safe and reliable AI behavior
- Mitigate hallucinations, prompt injection, and model misuse
- Ensure compliance with data privacy and enterprise requirements
- Implement monitoring and observability for AI systems in production
- Implement guardrails for agent actions (tool access control, execution boundaries)
- Prevent failure cascades in multi\-step agent
Educational Qualifications: \-
- Engineering Degree BE/ME/BTech/MTech/BSc/MSc.
- Technical certification in multiple technologies is desirable.
Skills: \-
Mandatory skills
Core AI Skills
- Strong understanding of LLM capabilities and limitations
- Experience with prompt engineering and structured output design
- Hands\-on experience with embeddings and vector search
- Familiarity with RAG architectures and when to apply them
- Experience designing agent\-based architectures (AgentCore concepts)
- Understanding of tool use, planning strategies, and memory mechanisms in LLM systems
Engineering Skills
- 5\+ years of related work experience
- Solid backend/system design fundamentals
- Experience building and deploying production\-grade systems
- Ability to debug complex issues, including probabilistic outputs
- Comfort working with APIs, pipelines, and data flows
Product Thinking
- Ability to translate user needs into effective AI solutions
- Strong intuition for balancing quality, latency, and cost
- Focus on delivering measurable product impact
Collaboration
- Communicates clearly across engineering and product teams
- Contributes to team knowledge and shared practices.
Good to have skills
- Evaluate agent performance across multi\-step tasks (task success rate, error propagation)
- Debug and optimize agent decision\-making and tool selection behavior
VeeRteq Solutions is an Equal Opportunity Employer
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 3,708 AI roles we're tracking, AI Engineering Manager positions make up 0% of the market. At VeeRteq Solutions Inc., 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 Engineering Manager roles pay a median of $249,650 based on 10 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
VeeRteq Solutions Inc. AI Hiring
VeeRteq Solutions Inc. has 6 open AI roles right now. They're hiring across AI Architect, AI Engineering Manager, AI/ML Engineer. Positions span Richmond, VA, US, US, Eden Prairie, MN, US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
Career Path
Common paths into AI Engineering Manager 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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