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About This Role
Applied AI Engineer (Agents & Systems Integrations)
SunMed Growers / Tidal Creek Growers
About the role
We’re building an AI-native operating layer for the business — not another chatbot experiment. As our Applied AI Engineer (Agents & Systems Integrations), you’ll design, ship, and run the agents and integrations that connect real workflows to our systems of record, using Model Context Protocol (MCP) servers, Claude, AWS, and business APIs. Think virtual teammates: durable agents that take work, call tools, update SoRs, and hand results back to people — then get better as more processes come online.
This isn’t a traditional software-engineer seat (no endless ticket queue, no feature factory). You’ll own the build-once patterns — MCPs, APIs, AWS-hosted agents/apps, Claude-powered workflows — and the practical craft of making AI teammates trustworthy enough that the org actually uses them. You bring the engineering fundamentals; we collaborate with you on AI design and build on top of them. You’ll work day to day with IT and the people who own each business process, and you’ll be regularly onsite at SMG/TCG. If you want to build agentic systems that run a real company, not a demo, this is that seat.
What you’ll own
· Design, build, and operate AI agents that execute real business workflows
· MCP / tool / API integrations into systems of record and internal services
· AWS-hosted agents and AI-created apps (build-once, reuse everywhere)
· Hand-off patterns so people can assign work to virtual teammates as capabilities mature
· Observability for every agent: structured logging, step-by-step traces of what an agent did and why, metrics, dashboards, and alerts that tell us when something breaks or drifts
· Reliability and incident response: health checks, retries and safe failure, a pause or kill switch, replaying a failed run to find the cause, and fixes backed by regression tests — production agents, not one-off demos
· Cost and quality monitoring: tracking AI usage and spend, response speed, error rates, and output quality over time, including after model or prompt changes
· Security and governance for agents: least-privilege access, secrets handling, audit logging, and clear rules for what data may and may not reach an outside AI model
· Partner with operators and process owners to turn messy workflows into agent-ready systems
Must-haves
· Strong software engineering fundamentals and API/integration chops: you arrive able to build, test, and ship production software on your own
· Comfortable in Python and/or TypeScript for production work
· Proven experience shipping agentic or tool-using AI systems (agents, copilots, or automation that calls tools/APIs — not just chatting with an LLM)
· Ability to own work end-to-end: design → build → deploy → monitor → improve
· Observability mindset: builds logging, tracing, metrics, and alerting into agents from day one, and can diagnose a failing agent from its telemetry
· Clear written and verbal communication with technical and non-technical partners
· Bias for practical, reusable systems over one-off glue
· Security-minded engineering: least-privilege access, safe handling of credentials and secrets, audit trails, and good judgment about company data and AI tools
Strongly preferred
· Hands-on with MCP (Model Context Protocol) or equivalent tool-calling agent frameworks
· Experience integrating with business SoRs / ERPs / SaaS APIs
· AWS (or similar cloud) for hosting agents, functions, or small apps
· Claude / Anthropic (or comparable frontier LLM) production usage
· Evaluation, guardrails, and operational discipline for AI in production
· Hands-on with observability tooling (for example OpenTelemetry, AWS CloudWatch, or an LLM tracing tool such as Langfuse) and with alerting and incident response
· Experience defining service-level targets (uptime, error rate, response time) and monitoring cost and quality for AI workloads
Nice-to-haves
· Prior work building “virtual teammate” / multi-agent patterns
· Data plumbing (queues, webhooks, light ETL) around agent workflows
· Familiarity with operations-heavy industries (manufacturing, logistics, regulated environments)
· Light frontend skills for internal operator UIs
Details
· Full-time W-2
· Hybrid (not fully remote); regularly onsite at SMG/TCG, exact schedule set with HR
· Comp: $115,000–$130,000
Pay: $115,000.00 - $130,000.00 per year
Benefits:
- 401(k) matching
- Dental insurance
- Employee assistance program
- Employee discount
- Flexible spending account
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Parental leave
- Vision insurance
Application Question(s):
- Show me code or a service you wrote in Python or TypeScript that runs in production. How is it tested and deployed?
- Tell me about connecting to a business system like an ERP or SaaS tool through its API (application programming interface). What was hard?
- Describe something you took from idea to design, build, deploy and ongoing support. What did you have to fix after launch?
- Tell me about something you built once that ended up being reused for other needs. How did you design it so that could happen?
- An agent will read and update our business systems. How do you keep it safe, and what company data should never go to an outside AI model?
- Walk me through an AI agent or tool-using system you built that real people used. What did it do, and what went wrong?
- How do you decide which tools an agent gets, and how do you stop it from calling the wrong one or looping?
- What have you built or used with MCP, a standard way to connect AI to tools and data? How did you handle sign-in and permissions?
- How do you know an agent is working, and how do you catch it getting worse after a change?
- What have you run in production with Claude or a comparable model, and how did you manage cost, speed and quality?
- How should a person assign work to an agent, check it, and take over when it's unsure?
- Have you built agents that work together or hand work to each other, or a long-running agent that acts like a teammate? How did you keep them from stepping on each other?
- At 2 a.m. an agent starts producing wrong results. How do you find out, and how do you find the cause from its logs and traces?
- What logging, metrics, tracing and alerting tools have you used, and how did you decide what to measure?
- How do you make sure a change doesn't break something that already works?
Work Location: In person
Salary Context
This $115K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Knight Island Management, 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($122K) sits 42% below the category median. Disclosed range: $115K to $130K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Knight Island Management AI Hiring
Knight Island Management has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Warwick, MD, US. Compensation range: $130K - $130K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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