Accelerating progress. Redefining possibilities. 

Position Overview

Reva is seeking a Product Manager to define and drive the roadmap for identity, policy, and authorization controls within AI and agentic systems. This role involves working directly with the VP of Product and cross-functional teams to deliver enterprise-grade security features that address emerging AI risks. The position offers significant ownership, exposure to enterprise customers, and the opportunity to work on cutting-edge AI security challenges in a startup environment.

Key Responsibilities

• Define and refine the product roadmap for identity, policy, and authorization controls in AI and agentic systems.
• Translate customer and security requirements into clear specifications, PRDs, and user stories.
• Collaborate with engineering teams to deliver adaptive access, runtimeguardrails, and identity-aware AI security solutions.
• Conduct competitive analysis across IAM and AI security products.
• Prototype workflows and product concepts using AI tools for early-stage validation.
• Support customer demos, proof of concepts (POCs), and onboarding with domain and product expertise.
• Drive execution with a bias for action, focusing on rapid iteration and continuous learning.

Required Skills and Experience

• 3–4 years of experience as a Product Manager or Product Owner in cybersecurity (IAM, PAM, ZTNA, or policy enforcement).
• Strong understanding of policy-as-code and authorization models (RBAC,ABAC, PBAC, OAuth2, OIDC, SAML, JWT).
• Familiarity or strong curiosity about AI/agentic AI security challenges such as prompt injection, data leakage, and identity spoofing.
• Practical experience with prototyping tools like Lovable, Replit, Figma, or Bolt.
• Ability to collaborate with technical teams and translate complex security concepts into usable features.
• Excellent communication skills, both written and verbal.

Preferred Qualifications

• Master’s degree in technology or MBA.
• Relevant cybersecurity certifications such as CISSP, CISM, CCSP, or IAM specific credentials.
• Experience in early-stage startups or high-growth technology environments.
• Passion for AI security and policy innovation with hands-on problem-solving ability.

Ideal Candidate Profile

The ideal candidate is a proactive cybersecurity product manager with a strong foundation in identity, policy, and authorization who is eager to explore the evolving world of AI security. This person thrives in ambiguity, prototypes ideas quickly, and excels at turning emerging risks into practical security solutions.

Perks and Benefits

• Work on cutting-edge AI security challenges shaping enterprise AI adoption.
• Direct mentorship from senior product leaders and co-founders.
• Exposure to enterprise customers, CISOs, and early adopters of AI security.
• Startup culture emphasizing ownership, autonomy, and rapid growth opportunities.
• Flexible working setup (remote or hybrid depending on location).

Position Overview

At Reva, we are building AI-driven systems that intelligently recommend, design, and enforce access and policy controls. As a Senior AI Engineer, you will lead the design and implementation of ML and RAG-based recommendation engines and responsible for the end-to-end architecture to integrate these capabilities into production platforms.

This role is ideal for a strong engineer with deep AI/ML expertise who understands that models are only useful when they are reliable, scalable, and exposed cleanly through real systems.

Key Responsibilities

• Design and build ML and RAG-based recommendation engine
• Implement semantic search, embeddings, reranking, and relevance evaluation.
• Continuously improve recommendation quality using feedback loops and metrics.
• Own AI features from data to deployment, including:
  • Data ingestion and preprocessing
  • Feature engineering and embeddings pipelines
  • Model training, evaluation, and serving
  • API exposure and integration into backend systems
• Collaborate with platform and backend teams to integrate AI services into production workflows.
• Design and work with batch and streaming data pipelines supporting ML workloads.
• Ensure data quality, versioning, and reproducibility.
• Optimize inference latency and system reliability.

Required Skills and Experience

AI / ML

• Strong experience building production ML systems, not just experiments.
• Hands-on experience with:
  • Recommendation systems
  • Retrieval-Augmented Generation (RAG)
  • Embeddings and vector search
• Familiarity with model evaluation, monitoring, and iteration.

Engineering & Architecture

• Strong backend engineering fundamentals.
• Experience designing scalable, distributed systems.
• Ability to expose ML systems via clean APIs (REST / gRPC).

Data & Pipelines

• Experience with data pipelines and ML workflows.
• Solid understanding of data stores, feature generation, and data lifecycle management.

Tech Stack (Representative, not mandatory)

• Languages: Python, Go, or Java
• ML: PyTorch, TensorFlow, scikit-learn, Hugging Face
• RAG Stack: LangChain, LlamaIndex, vector databases (FAISS, Milvus, Pinecone)
• Backend: FastAPI, Flask, Gin
• Data: Kafka, Spark, Airflow
• Infra: Docker, Kubernetes, Cloud (AWS/GCP/Azure)

Nice to Have

• Experience working with LLMs or GenAI platforms.
• Prior experience building policy, access control, or decisioning systems.
• Exposure to MLOps, model monitoring, and production reliability.
• Startup or fast-paced product environment experience.

Position Overview:

We are seeking a highly experienced and hands-on Principal DevOps Engineer to lead the design, automation, scalability, security, and operational excellence of our cloud platform.
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This role is intended for a senior technical leader who remains deeply involved in implementation. The successful candidate will be responsible for building and maintaining infrastructure as code, CI/CD platforms, cloud-native infrastructure, deployment automation, and developer enablement capabilities across the organization.
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This is not a management-only or architecture-only role. The Principal DevOps Engineer is expected to actively write and maintain infrastructure code, pipeline code, automation frameworks, and platform tooling while providing technical leadership and strategic direction.

Key Responsibilities

• Lead the architecture and evolution of the organization's cloud and platform infrastructure.
• Design, build, and maintain highly available, secure, and scalable AWS environments.
• Develop and maintain Infrastructure as Code using OpenTOFU and reusable infrastructure modules.
• Design, implement, and optimize CI/CD platforms and deployment automation frameworks.
• Build and maintain GitLab-based development workflows, repository standards, and release processes.
• Develop and maintain CodeBuild, CodePipeline, container build pipelines, artifact management processes, and deployment automation.
• Lead Kubernetes and container platform initiatives, including platform reliability, security, scalability, and operational excellence.
• Drive infrastructure standardization, governance, disaster recovery, and cloud security best practices.
• Implement monitoring, observability, alerting, and incident response capabilities.
• Troubleshoot complex infrastructure, networking, deployment, and production issues.
• Mentor engineers and establish DevOps and Platform Engineering best practices across teams.

Hands-On Engineering Expectations

The successful candidate will spend a significant portion of their time actively developing and maintaining:

• OpenTOFU modules and infrastructure code.
• CI/CD pipeline definitions and deployment automation.
• GitLab integrations and workflow automation.
• Container build, deployment, and release processes.
• Infrastructure automation scripts and platform tooling.
• Kubernetes deployment and operational automation.
• Monitoring, observability, and operational engineering solutions.

The role requires an engineer who is comfortable working directly in code repositories and contributing production-ready infrastructure and automation code on a daily basis.

Required Qualifications

• 10+ years of experience in DevOps, Cloud Engineering, Site Reliability Engineering, or Platform Engineering.
• Deep expertise in AWS cloud architecture and operations.
• Strong hands-on experience with Infrastructure as Code, particularly OpenTOFU or Terraform.
• Extensive experience building and maintaining enterprise CI/CD platforms.
• Strong experience with GitLab, source control workflows, and repository management.
• Proven experience with Kubernetes, containerized workloads, and cloud-native platforms.
• Strong understanding of networking, load balancing, DNS, VPNs, security, and cloud infrastructure design.
• Experience with monitoring, observability, logging, and operational excellence practices.
• Strong scripting and automation skills using Python, Bash, Go, or similar technologies.
• Experience supporting production systems requiring high availability, scalability, and security.

Preferred Qualifications:

• AWS Professional-level certifications.
• Kubernetes certifications (CKA, CKAD, or CKS).
• Experience building internal developer platforms and self-service infrastructure capabilities.
• Experience implementing GitOps and platform engineering practices.
• Experience operating large-scale multi-environment cloud platforms.

What Success Looks Like

The successful candidate will serve as the organization's technical authority for cloud infrastructure, DevOps, and platform engineering. They will drive automation-first practices, improve deployment velocity, strengthen reliability and security, and build scalable infrastructure platforms that enable engineering teams to deliver software efficiently and safely.
Most importantly, they will lead through technical contribution, regularly writing and maintaining infrastructure, automation, and deployment code rather than operating solely in an advisory capacity.