Engineer II, AI/ML

Enphase Energy is a global energy technology company and a leading provider of solar, battery, and electric vehicle charging products. Founded in 2006, our innovative microinverter technology revolutionized solar power, making it a safer, more reliable, and scalable energy source. Today, the Enphase Energy System enables users to make, use, save, and sell their own power. Enphase is also one of the most successful and innovative clean energy companies in the world, with more than 80 million products shipped across 160 countries.

Join our dynamic teams designing and developing next-gen energy technologies and help drive a sustainable future!

This role at Enphase requires working onsite 3 days a week, with plans to transition back to a full 5 day in office schedule over time.

About the role

As Security Engineer, AI Security, you will work at the intersection of AI and cybersecurity, securing the AI/ML and Gen-AI systems that Enphase builds and deploys at global scale, and applying AI to strengthen how we detect, triage, and respond to security threats across our cloud, product, and enterprise environments. You will help protect AI systems serving millions of Enphase customers while building AI-powered tooling that multiplies the effectiveness of our security teams.

What you will do

• Perform security architecture reviews and threat modeling of AI/ML and Gen-AI applications, including LLM-based agentic systems, RAG pipelines, and predictive maintenance platforms.
• Assess and mitigate AI-specific risks such as prompt injection, jailbreaking, model/data poisoning, sensitive data leakage, insecure output handling, and excessive agency, aligned to frameworks such as the OWASP Top 10 for LLM Applications and MITRE ATLAS.
• Build and run adversarial testing / AI red teaming exercises against internal LLM applications, agents, and APIs, and drive remediation with development teams.
• Secure the AI/ML supply chain and MLOps pipeline: model provenance, training data governance, dependency and container security for ML workloads, and secure model deployment across multi-cloud environments.
• Define and enforce guardrails, access controls, and monitoring for LLM connectors, plugins, and third-party AI integrations (including SaaS AI tools connected to corporate identity and data).
• Contribute to AI governance: usage policies, risk assessment processes for new AI use cases, and alignment with emerging regulatory expectations (e.g., EU AI Act, CRA, NIS2).

AI for Security (Security Automation & Detection)

• Design and build LLM-powered security tooling — incident triage assistants, alert enrichment and summarization, detection engineering copilots, and automated report generation for SOC and product security teams.
• Develop AI/ML models and pipelines for anomaly detection, threat detection, and abuse/fraud detection across cloud telemetry, IoT device fleets, and application logs.
• Integrate Gen-AI capabilities into existing security workflows (SIEM, SOAR, vulnerability management, bug bounty triage) with attention to accuracy, hallucination control, and human-in-the-loop review.
• Build agentic automation for repetitive security operations tasks while ensuring appropriate least-privilege access, auditability, and safe failure modes.
• Measure and improve the effectiveness of AI-driven security tooling: evaluation harnesses, precision/recall on detections, and analyst feedback loops.

Cross-functional

• Partner with data science, platform, cloud, DevOps, firmware, and product security teams to embed security into AI systems from design through deployment.
• Participate in incident response for AI-related security events and contribute to root cause analysis and corrective actions.
• Stay current on AI security research, attack techniques, and defensive tooling, and share knowledge across the security organization.

Who you are and what you bring

• BE/BTech in CS/ECE/EEE or equivalent from a top-tier institution IIT's with a strong academic record.
• 1+ years of experience in security engineering, AI/ML engineering, or a closely related field, with demonstrated work at the intersection of the two (professional, research, open source, or CTF/red team experience all count).
• Strong hands-on Python programming skills; comfort working with data pipelines, APIs, and cloud services (AWS/GCP/Azure).
• Working knowledge of LLMs, prompt engineering, RAG architectures, and agentic frameworks — and of how these systems fail or can be attacked.
• Familiarity with AI security threat frameworks such as OWASP LLM Top 10, MITRE ATLAS, or NIST AI RMF.
• Solid grounding in core security concepts: authentication/authorization, secrets management, network and application security, secure SDLC, and cloud security fundamentals.
• Understanding of ML fundamentals (model training, evaluation, deployment) sufficient to reason about model behavior, data flows, and attack surface.
• Systematic problem-solving approach, strong written and verbal communication, and a sense of ownership and drive.
• Ability to collaborate across security, engineering, and data science teams and translate risk into practical engineering guidance.

Nice to have

• Experience red teaming or penetration testing LLM applications or ML systems.
• Experience building security automation with LLM APIs (Anthropic, OpenAI, Bedrock, Vertex, etc.) or agent frameworks.
• Exposure to detection engineering, SIEM/SOAR platforms, or SOC operations.
• Familiarity with IoT/embedded or energy-sector security, and securing ML at the edge (on-device inference, firmware-delivered models).
• Contributions to AI security research, open-source tooling, or bug bounty findings on AI systems.