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About This Role
JOB SCOPE:
The Forward Deployment Engineer (FDE) Intern will work within the SearsKAIros Applied AI Division at TransformCo, partnering directly with business and operational teams to identify opportunities for AI\-driven automation and workflow optimization. This role combines software engineering, AI application development, operational problem solving, and stakeholder engagement. The intern will rapidly prototype, deploy, and improve AI agents that drive measurable operational and financial outcomes across various business functions including Sears Home Services, Kenmore, and Central Finance.
JOB SUMMARY:
The Forward Deployment Engineer (FDE) Intern is responsible for embedding with operational teams to understand existing workflows, identify inefficiencies, and develop production\-ready AI agent solutions using the SearsKAIros Applied AI platform. This role requires a highly adaptable individual who can work in ambiguous environments, rapidly prototype solutions, integrate AI technologies, measure performance outcomes, and support adoption of AI\-enabled workflows across the organization.
REPORTS TO:
CEO, Sears KAIros
Responsibilities/Skills/Experience Requirements
JOB DUTIES/RESPONSIBILITIES:* Shadows business operators, dispatchers, planners, analysts, and finance partners to understand and document current manual processes and workflows
- Analyzes operational pain points and translate business workflows into AI agent specifications
- Designs, builds, tests, and deploys AI agents on the SearsKAIros Applied AI platform
- Integrates AI agents with internal systems and external AI models including Claude, OpenAI Realtime, Gemini, and MCP servers
- Prototypes and iterates quickly, owning the full lifecycle of AI solutions from concept through production deployment
- Develops evaluations, guardrails, monitoring, and rollback procedures for deployed AI agents
- Builds dashboards, KPI tracking, and reporting tools to measure operational and business impact
- Collaborates with engineering teams, operational leadership, and field teams to support implementation and adoption
- Trains users, respond to escalations, and incorporate operational feedback into continuous improvements
- Documents implementation patterns, best practices, and reusable processes to accelerate future AI deployments
- Presents workflow analyses, solution recommendations, and performance outcomes to stakeholders and leadership teams
JOB REQUIREMENTS:* Some undergraduate education
- 1\-2 years of related experience
- 18 years of age or older
REQUIRED SKILLS:* Strong software engineering fundamentals with proficiency in Python and TypeScript
- Ability to quickly understand and modify unfamiliar codebases
- Hands\-on experience building applications using Large Language Models (LLMs), including prompting, tool usage, function calling, retrieval systems, agent loops, and evaluation frameworks
- Experience deploying or shipping AI\-based solutions or applications
- Working knowledge of SQL and data analysis tools, including the ability to independently query and analyze data from platforms such as Snowflake
- Strong problem\-solving skills and ability to work effectively in ambiguous, fast\-paced environments
- Excellent written and verbal communication skills, including the ability to document workflows and present findings clearly to both technical and non\-technical audiences
- Ability to collaborate directly with operational stakeholders and business users
- Currently enrolled in a Bachelor s, Master s, or PhD program in Computer Science, Electrical Engineering, Operations Research, Applied Mathematics, or a related quantitative field
- Authorized to work in the United States and able to complete required background checks prior to systems access
PREFERRED SKILLS:* Familiarity with MCP, agent orchestration frameworks, or related technologies such as LangChain, LlamaIndex, or OpenAI Agents SDK
- Prior internship or project experience deploying AI or machine learning solutions in enterprise or operational environments
- Exposure to field service operations, logistics, supply chain, contact center operations, or finance workflows
- Experience working with voice agents, real\-time APIs, or telephony integrations
- Demonstrated ability to rapidly prototype and operationalize AI solutions with measurable business impact
Years Experience
1 \- 2 Years Experience
Travel Requirements
None
Country
United States
Work\-In City
REMOTE
Work\-In State
PA
Work\-In Postal Code
REMOTE
Business
Transformco Home Services \- Support
Job Function
Information Technology
Employment Category
Intern, Full\-time
Compensation Range
N/A
Additional Compensation Explanation
N/A
EEO/EOE Footer
Equal Opportunity Employer / Disability / Vet.
Posting Tags
\#Remote, \#Technology
Company Brand
Sears Home Services
Location City
HOFFMAN ESTATES
Role Details
About This Role
AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.
Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.
Across the 3,823 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Sears Home Services, this role fits into their broader AI and engineering organization.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
What the Work Looks Like
A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
Skills Required
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?
Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
Compensation Benchmarks
AI Agent Developer roles pay a median of $245,040 based on 106 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,880.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Sears Home Services AI Hiring
Sears Home Services has 1 open AI role right now. They're hiring across AI Agent Developer. Based in PA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.
Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.
What to Expect in Interviews
Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.
When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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