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We are seeking a Senior Application Software Developer and Team Lead to join the AI4Support team and build scalable, cloud-based machine learning and AI solutions. This role will focus on developing and productionizing ML models and intelligent applications leveraging machine learning, NLP, large language models, and generative AI. The successful candidate will work closely with applied scientists, Machine learning engineers, software engineers, and cross-functional teams to transform complex data and business challenges into reliable, scalable solutions that deliver measurable customer impact.
The ideal candidate is a hands-on engineer and technical leader who can tackle complex, ambiguous problems and drive solutions from experimentation through production. This includes contributing to model development and evaluation, Java microservice orchestration, ML system design, deployment, monitoring, and optimization, while helping establish strong MLOps, testing, observability, and cloud
Adheres to and suggests improvements to all phases of the application software development lifecycle. Utilizes working knowledge to develop new application features and enhancements following design specifications, and develops documents to clarify application design and code. Leads code reviews in designated application areas to help drive improvements. Conducts debugging and troubleshooting to identify and fix moderately complex application issues. Develops fixes for identified issues. Troubleshoots and resolves moderately complex issues related to application programming interface (API) functionality, integration, and service-to-service interactions. Designs and develops application software, systems, and services aligned to pre-defined application architecture. Collaborates within and beyond the immediate team to understand customer use cases and align application solutions. May provide technical guidance and support to customers regarding application-related issues, independently. Collaborates with the team to establish and follow application development practices, secure coding standards, and code quality. Performs periodic maintenance and testing operations for application systems that require upgrading or patching (e.g., for critical vulnerabilities).
Key Responsibilities
Application Software Development:
- Drives medium features and quality improvements with measurable performance and reliability outcomes.
- Provides global production support for critical issues with minimal guidance; improves playbooks/alerts.
- Troubleshoots and debugs non-routine issues spanning application layers and common OCI services; proposes fixes.
- Partners with QA to define test strategy (unit/integration) for features; ensures consistency across modules.
- Considers trade-offs in existing architecture; proposes moderate design improvements.
- Translates ambiguous requirements into scalable designs and implementation plans.
- Defines and stabilizes API contracts and dependencies for the application area.
- Recommends build priorities within the team using data from incidents, telemetry, and customer feedback.
- Systematically incorporates external feedback (forums, live-site reports) into backlog refinement.
- Mentors junior engineers; coordinates cross-functionally on feature delivery.
Software Development and Coding - Design, Testing, and Optimization:
- Designs software solutions and analyzes and helps identify requirements to achieve business and operational goals, independently.
- Adheres to and suggests improvements to all phases of the software development lifecycle.
- Utilizes working knowledge to develop new software features and enhancements following design specifications and develops documents to clarify software design and code.
- Leads code reviews in designated areas to help drive improvements.
- Conducts debugging and troubleshooting to identify and fix moderately complex software issues.
- Develops fixes for identified issues.
- Implements software testing (e.g., functional and non-functional testing), quality assurance processes, software error logging, monitoring, and observability for effective debugging, ensuring review by manager and/or lead throughout the process.
- Exercises judgment and discretion to conduct performance profiling and optimization of coding.
- Troubleshoots and resolves moderately complex issues related to application programming interface (API) functionality and integration.
- Implements moderately complex API versioning, lifecycle, and interoperability strategies.
Software Architecture - Software System Structural Design:
- Designs and develops software, systems, and services aligned to pre-defined system architecture.
- Develops working knowledge of software architecture decisions and best practices.
- Collaborates with team leads to review work to ensure alignment with software architecture.
- Implements moderately complex performance optimization and scalability strategies in software design.
Issue/Defect Collaboration - Software Products Support:
- Collaborates within and beyond immediate team to understand customer issues and align solutions.
- May provide technical guidance and support to customers regarding customer-reported issues, independently.
- Coaches, mentors, and guides others to advocate for customers' interests and suggests product enhancements based on feedback.
- Influences team to ensure customer satisfaction through timely resolution of issues and effective communication.
- Implements customer issue and/or defect handling and training processes, independently.
- Investigates and troubleshoots complex product maintenance issues to ensure customer agreement on short- and long-term solutions (e.g., future enhancements).
Practices and Standards Compliance - Security and Compliance:
- Collaborates with the team to establish and follow development practices and coding standards.
- Exercises judgment and discretion to ensure code quality and adherence to broad acceptance criteria during development.
- Keeps up-to-date with industry best practices and applies them to software development processes.
- Implements secure coding practices to prevent security vulnerabilities.
Development Operations - System Maintenance:
- Performs periodic maintenance and testing operations for systems that require upgrading or patching (e.g., for critical vulnerabilities).
- Exercises judgment and discretion to drive improvements, ensure automation, testing, and debugging of systems to ensure service/product availability, health, support, and reliability.
Core Responsibilities
Planning & Execution:
- Independently manages work, monitoring timelines and deliverables to ensure projects or initiatives stay on track and meet requirements.
- Proactively prioritizes work and adapts to resource or timeline shifts, suggesting adjustments to maintain project efficiency.
Collaboration & Partnership:
- Collaborates across teams to align on expectations and achieve shared objectives.
- Builds and maintains a comprehensive understanding of business, stakeholder, and/or customer needs to build and support effective partnerships.
- Actively listens to diverse perspectives and asks questions to ensure understanding of others.
Problem Solving:
- Independently identifies and addresses standard and non-standard issues in accordance with standard practices, escalating more complex issues as appropriate.
- Analyzes data and/or information from multiple sources to troubleshoot standard and non-standard errors.
- Contributes to knowledge sharing and best practices.
Continuous Learning:
- Embraces continuous learning by actively seeking to build knowledge and new skills and/or tools and staying current with industry trends and best practices.
- Seeks out and leverages feedback and training to improve skills.
- Contributes to a culture of continuous learning and knowledge sharing with team members.
Continuous Improvement:
- Develops ideas and recommends updates to increase the efficiency and effectiveness of processes, protocols, and workflows within a team.
- Seeks input from team members on alternative approaches and methods for improving work.
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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 9,079 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Oracle, 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 in Demand for This Role
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 $215,000 based on 203 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Oracle AI Hiring
Oracle has 11 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Software Engineer, AI Product Manager. Positions span Nashville, TN, US, Santa Clara, CA, US, Nashville, TN.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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