Applied AI Engineer

This is a fully remote position, open to applicants in India.

📋 Description

• Design and refine behaviors of AI agents within real-world cybersecurity and software engineering workflows.

• Create multi-step workflows for agents that incorporate tool invocation, branching logic, retries, validation, and human-in-the-loop controls.

• Develop schemas for tools, execution strategies, context creation, memory, and retrieval systems.

• Experiment with prompting techniques, strategies for model interaction, tool usage patterns, and context engineering.

• Analyze failures in agents and translate those failure modes into enhancements for products and engineering.

• Establish guardrails, policy frameworks, and mechanisms for safe execution in security-sensitive environments.

• Design and conduct evaluations to measure agent quality, reliability, regressions, and edge cases.

• Create evaluation pipelines, testing frameworks, scoring systems, and high-quality datasets.

• Develop feedback mechanisms that link real-world task data and production failures to evaluation and development processes.

• Assess production traces and agent behaviors to enhance solve rates, usefulness, and reliability.

• Design and construct scalable backend services that support AI agents and cybersecurity workflows.

• Develop high-scale, multi-tenant systems that securely accommodate various customer environments.

• Engage with microservices, asynchronous execution, event-driven architectures, and distributed systems.

• Develop layers for ingestion, indexing, retrieval, and agent memory specific to security data.

• Create reliable execution systems with observability, traceability, monitoring, and data quality assurances.

• Build and maintain integrations with SIEM, SOAR, EDR, and NDR platforms.

• Take ownership of features from architecture and implementation to deployment, monitoring, debugging, and production iteration.

• Collaborate with teams across product, research, infrastructure, security, and customer-facing roles.

• Assist in shaping user interfaces and workflows for engaging with AI agents.

• Contribute to architectural decisions and the technical direction of projects.

⛳️ Requirements

• 4–7 years of professional software engineering experience; the broader role description also states 5–8 years.

• Extensive backend development experience, ideally with Python, Go, or Node.js.

• Solid understanding of the fundamentals of distributed systems.

• Experience with microservices, APIs, asynchronous execution, and event-driven architectures.

• Practical experience with LLMs, Generative AI, or Applied AI.

• Familiarity with prompt engineering, RAG, embeddings/vector databases, LLM evaluation, tool/function invocation, or agent frameworks.

• Proven experience in building and deploying software for production.

• Strong problem-solving and debugging capabilities.

• An ownership mindset with the ability to drive a problem from inception to deployment.

• Highly skilled in Python and adept with modern AI/ML tools.

• Experience in developing and delivering ML/LLM-powered products or applying LLMs to engineering challenges.

• Background in LLMs, prompt engineering, RAG, embeddings, model evaluation, or agentic systems.

• Strong software engineering principles and capability to write clean, maintainable, and well-tested code.

• Bonus: familiarity with LangGraph, LangChain, or similar agent frameworks.

• Bonus: experience with model evaluation, fine-tuning, or code-generation models.

• Bonus: experience in developer tools or AI coding systems.

• Bonus: experience with cybersecurity products, particularly SIEM, SOAR, EDR, NDR, or security data pipelines.

• Bonus: experience with AWS, GCP, Azure, Docker, Kubernetes, and CI/CD processes.

• Bonus: experience in building AI-agent evaluation frameworks, benchmark datasets, automated testing systems, observability, tracing, or production LLM monitoring.

• A strong academic background in Computer Science or a related field is preferred; graduates from IITs or other top-tier engineering institutions are favored.

🏝️ Benefits

• Build an AI-first cybersecurity platform from the ground up.

• Work at the intersection of AI, cybersecurity, and distributed systems.

• Address challenges where there isn't always a clear solution.

• Collaborate directly with founders, researchers, security specialists, and engineering leaders.

• Take ownership of significant problems from start to finish and witness your contributions go into production.

• Join an early-stage team where your technical decisions and ideas can have a substantial impact.

• Experience a fast-paced environment where you can learn quickly and create impactful systems.