דרושים » תוכנה » R&D Manager

Be a key part in the company EPM Agents group, owners of a combined solution for application control, privilege management, and threat detection. We are searching for a Team Manager to lead Cross-Platform Agents Infrastructure team in an agile-oriented environment. leading the journey to design, develop and innovate our EPM Agents.
The responsibilities will include:
Producing valuable and qualitative deliverables for our customers in a timely fashion
Providing technical authority to your team by demonstrating a hands-on leadership style
Hiring, mentoring and driving the professional growth of the team members
Overseeing the overall development, code quality, and production of the team
Permanent strive to keep the code up-to-date and at the highest quality, innovation and security levels
Working hand-in-hand with architects, product owners, and other teams to build robust solutions.

We are looking for a Platform Manager, AI Transformation to lead our strategic transition into an AI-native engineering organization. In this high-impact role, you will establish and lead the AI Platform Enablement Team, turning AI from isolated experimentation into a scalable, secure, and standardized engineering capability. You will be responsible for defining how engineers collaborate with AI agents, introducing autonomy safely, and building reusable AI foundational capabilities. This role combines technical leadership in platform engineering with AI systems thinking and organizational enablement. You will balance the speed of innovation with essential safety, security, and reliability standards. The team works in full collaboration with cross-functional engineering groups to embed AI into daily development and operations workflows. We are looking for a hands-on technical leader who can design complex systems and also get into the details when needed to turn ambiguous ideas into production-grade capabilities.
Your Impact
Own and deliver foundational capabilities including Service-Level AI Agents, AI-assisted incident management, and AI Developer Agents for PR creation and refactoring.
Manage the AI Control Plane / MCP Infrastructure, ensuring secure, auditable interfaces with clear permission boundaries.
Roll out training programs and certification frameworks to establish AI as a first-class engineering skill.
Design and implement systems with human-in-the-loop controls, auditability, and cost control standards for safety and observability.
Build and maintain mechanisms that improve AI systems using feedback loops from code reviews, incidents, and developer feedback.

As a Deputy Program Manager (DPM), you will serve as a Director-level member of the Program Management Team (PMT) and work across highly matrixed organizations to achieve program objectives. The DPM will partner closely with the Program Manager (PM), customer stakeholders, and cross-functional Oracle teams to drive execution of mission-critical initiatives in complex and dynamic environments.

The ideal candidate will bring strong leadership capabilities, experience managing complex cloud or infrastructure programs, and a proven ability to collaborate across technical, operational, and business organizations. The candidate should possess a strong understanding of cloud environments, SaaS operations, technology transformations, migrations, and the technology delivery lifecycle.

Key Responsibilities
Lead and collaborate across diverse, matrixed teams to achieve PMT objectives
Support execution of large-scale cloud and infrastructure programs for Public Sector customers
Identify, assess, and manage program risks, issues, dependencies, and opportunities across multiple lines of business
Provide recommendations to improve integration and alignment across interfacing programs and projects
Support development of program-level briefings, documentation, and decision-support materials
Drive operational consistency through development of repeatable PMT processes and best practices
Partner with customer stakeholders and internal Oracle organizations to support successful program delivery
Ensure timely delivery of contractual deliverables and overall program commitments
Lead internal and external collaboration, communication, and stakeholder engagement activities
Support program governance, operational reporting, financial tracking, and strategic planning activities

We are looking for a Principal AI Engineering Lead to own and drive our AI engineering maturity journey across a 100-person R&D, QA, and DevOps organization.
This is a hands-on individual contributor role with outsized influence - you will be the internal expert, practitioner, and change agent who takes us from inconsistent AI tool adoption to a fully agentic, multi-phase autonomous engineering capability.
You will not be managing people. You will be changing how 100 engineers work.
This role is equal parts engineering, enablement, and architecture. You will write real code, build real agent workflows, and make the abstract concrete - turning a defined AI maturity framework into daily practice across Java/JVM, Python, C#/.NET, Oracle PL/SQL, and C++ codebases.
Scope of your position:
AI Adoption & Enablement
Audit current AI tool usage across R&D, QA, and DevOps - identify where adoption is genuine vs. nominal
Establish and maintain CLAUDE.md-equivalent constitution files: encoding team conventions, architectural standards, testing patterns, and security policies so AI tools produce consistent, codebase-aware output from day one
Drive daily active usage above 70% across the engineering org, measured by tool telemetry - not seat count
Design and deliver hands-on enablement: prompt engineering, output validation, effective task decomposition, and AI-assisted debugging across our primary stacks (Java/Spring, Python, C#/.NET, C++, Oracle PL/SQL)
Run monthly retrospectives to surface what context AI is still missing and close those gaps systematically
Agentic Infrastructure & Workflow Engineering
Architect and implement the infrastructure that makes autonomous agent execution safe: sandboxed execution environments, audit logging for all agent actions, and state checkpointing for mid-task recovery
Build and enforce the specification discipline: structured spec templates with machine-verifiable acceptance criteria, spec completeness gates before agent assignment, and a lightweight spec-driven development workflow appropriate for our codebases
Stand up self-verifying test loops - agents that write tests, implement, run CI, and iterate to green without human intervention - with coverage gates enforced in CI (80%+ on AI-generated code paths)
Shift CI/CD pipelines to increase build throughput: automated rollback, feature flags for deploy/release decoupling, and tiered review workflows (auto-merge → single reviewer → full review)
Deploy and tune Claude Code as the primary agentic coding platform, alongside evaluation and integration of other tools (GitHub Copilot, Cursor, or equivalent) where they complement the workflow

As a Data Science Manager, you will lead, mentor, and grow a team of applied data scientists working on machine learning models that power AI-driven cybersecurity solutions. You will own the end-to-end development lifecycle of AI projects, from exploratory research and experimentation through to scalable deployment and optimization. In this role, you will partner with engineering and product leadership to define technical strategies, prioritize initiatives, and ensure successful model delivery. You will also drive innovation across a broad spectrum of ML domains, including supervised/unsupervised learning, anomaly detection, and generative AI, while providing hands-on guidance and championing best practices in MLOps, data governance, and model evaluation.
Key Responsibilities
Lead, mentor, and grow a team of applied data scientists working on machine learning models that power AI-driven cybersecurity solutions.
Own the end-to-end development lifecycle of AI projects - from exploratory research and experimentation through scalable deployment and optimization.
Partner with engineering and product leadership to define technical strategies, prioritize initiatives, and ensure successful model delivery.
Drive innovation across a broad spectrum of ML domains including supervised/unsupervised learning, anomaly detection and generative AI.
Provide hands-on guidance in model development using tools such as scikit-learn, PyTorch, TensorFlow, and Hugging Face.
Ensure high-quality execution through strong code review practices, reproducibility, and model evaluation frameworks.
Champion best practices in MLOps, data governance, explainability, and monitoring.
Keep pace with academic and industry advances and translate cutting-edge research into productized capabilities.

This role requires a strong operational leader with experience managing large-scale cloud environments, developing technical talent, and driving continuous improvement through automation, standardization, and data-driven decision making.

Responsibilities
Leadership

Build, lead, and develop high-performing cloud operations and site reliability engineering teams.
Establish organizational goals, priorities, and operating rhythms aligned with OCI business objectives.
Foster a culture of operational excellence, accountability, collaboration, and customer focus.
Recruit, mentor, and develop technical leaders and individual contributors.