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About This Role
The Senior Manager will lead a team of engineers while serving as a hands\-on technical leader responsible for defining, building, and operating next\-generation AI systems on Oracle Cloud Infrastructure (OCI). This leader will set the architecture and engineering direction for production\-grade agentic AI platforms, autonomous workflows, scalable inference infrastructure, and enterprise AI applications used in large\-scale, business\-critical environments.
This role requires an experienced engineering manager who can build and develop high\-performing teams, translate ambiguous product and platform goals into a durable technical strategy, and drive execution across multiple organizations. The successful candidate will be accountable for hiring, mentoring, performance management, technical planning, and delivery, while remaining actively involved in system design, prototyping, coding, code reviews, operational readiness, and incident follow\-up.
The ideal candidate combines deep distributed\-systems expertise with practical, hands\-on experience building AI agents and AI\-native applications. This includes developing and orchestrating LLM\-based agents, tools, APIs, memory systems, retrieval pipelines, evaluations, guardrails, and cloud\-service integrations. The candidate should be comfortable writing production code, debugging complex systems, and guiding engineers through difficult architectural and implementation decisions.
The expectation is to lead the team in shipping, scaling, and operating reliable, secure, observable, and cost\-efficient AI systems, while raising both the engineering and management bar across the organization. This leader will establish strong execution practices, promote operational excellence, and ensure the team delivers measurable business and customer outcomes.
Responsibilities
- Lead and develop a team responsible for OCI AI platform capabilities, including agent execution, inference, orchestration, evaluation, and observability.
- Set the technical direction for production\-grade agentic AI systems that support reasoning, planning, tool use, multi\-step workflows, and human escalation.
- Remain hands\-on in architecture, prototyping, coding, debugging, and code reviews for critical AI\-agent components.
- Guide the development of services for tool calling, memory, context management, MCP integration, retrieval, multi\-agent coordination, policy enforcement, and evaluation.
- Own delivery across distributed systems optimized for reliability, performance, security, cost, and multi\-tenant operation.
- Translate broad goals into roadmaps, staffing plans, milestones, and measurable outcomes.
- Partner across infrastructure, security, data, product, and application teams to drive execution.
- Establish AgentOps and LLMOps practices for tracing, monitoring, testing, safety guardrails, versioning, and production readiness.
- Recruit, coach, and retain engineers while managing performance and developing senior technical leaders.
Own production outcomes, including reliability, security, cost efficiency, supportability, and delivery predictability.
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Required Qualifications
- Bachelor's, Master's, or Ph.D. in Computer Science, AI/ML, Engineering, or a related field, or equivalent experience.
- 8\+ years of software engineering experience, including ownership of production systems.
- 2\+ years of engineering management experience, including hiring, coaching, performance management, and delivery ownership.
- Proven ability to lead teams while remaining technically engaged in design, coding, reviews, debugging, and operations.
- Deep experience with distributed systems, cloud platforms, or AI/ML infrastructure.
- Hands\-on experience building AI agents, autonomous workflows, tool\-using systems, or multi\-step orchestration.
- Experience with frameworks such as LangGraph, LangChain, CrewAI, AutoGen, LlamaIndex, or similar tools.
- Strong understanding of LLM patterns, including tool calling, RAG, memory, context management, evaluation, and safety.
- Strong Python skills and experience with Kubernetes, Docker, observability, scalability, and fault tolerance.
- Strong understanding of AI security, governance, access control, auditability, and operational risk.
Excellent communication and cross\-functional leadership skills.
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Preferred Qualifications
- Experience managing teams that build AI platforms, agent runtimes, inference systems, or developer platforms.
- Experience with GPU inference optimization, model serving, workflow engines, or multi\-tenant cloud services.
- Experience integrating AI systems with enterprise APIs, databases, identity systems, vector stores, and policy layers.
- Experience with agent evaluation, adversarial testing, regression gates, and production observability.
- Experience using AI\-assisted development tools such as Codex, Claude Code, Cursor, or Copilot.
- Experience in enterprise, cloud infrastructure, regulated, or mission\-critical environments.
- Experience in data science and applied machine learning, including classical ML techniques, deep learning models, model evaluation, and production deployment.
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 4,317 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 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 $240,000 based on 96 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
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.
Oracle AI Hiring
Oracle has 17 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Agent Developer, Research Scientist. Positions span US, Nashville, TN, US, Santa Clara, CA, US.
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 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 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).
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 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.
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