Senior AI Engineer

About the job

Company Description

AIBound is revolutionizing AI security with the industry's first unified control plane for secure AI adoption. We discover, test, and protect each AI model, agent, and identity - catching AI risks before impact so enterprises can innovate safely and at speed. As AI adoption outpaces security across global organizations, AIBound eliminates the dangerous gap between innovation and protection.

Led by our CEO and founder, the former CISO at Palo Alto Networks and Workday, AIBound brings together a world-class team of cybersecurity veterans who have secured some of the world's most advanced enterprises. We're a fast-growing company backed by leading investors, positioned at the critical intersection of AI innovation and enterprise security - one of the most strategic technology frontiers of our generation.

Join us in building the future of AI security, where cutting-edge artificial intelligence meets battle-tested cybersecurity expertise.

About the Role

We are looking for a AI-native Senior Software Engineer, Full-stack to join our core engineering team. You will architect, build, and ship complex product features across the full stack, working with Go, TypeScript, React, PostgreSQL, Kubernetes, and GCP.This is a highly hands-on role with significant product ownership. You will take problems from idea and architecture through implementation and production, while helping establish the engineering standards and architecture as the platform scales.

We are looking for an AI-native Senior AI Engineer to join our core AI engineering team. You will design, build, and ship production AI systems, including LLM-powered features, AI agents, MCP servers and integrations, RAG pipelines, evaluation frameworks, and machine learning models, working with Python, PyTorch, PostgreSQL, and GCP.

This is a highly hands-on role with significant product ownership. You will take problems from idea and rapid prototype through data pipelines, model development, and deployment to production, while helping establish the engineering standards, MLOps practices, and AI architecture as the platform scales.

Preference will be given to candidates who can join in 30 days or less

Responsibilities

• Architect, build, and ship end-to-end AI-powered features, from early prototype through production
• Build applications on top of LLMs, including prompt design, tool/function calling, structured outputs, and agentic workflows
• Design and implement retrieval-augmented generation (RAG) systems, including document ingestion, chunking, embeddings, vector search, and re-ranking
• Develop evaluation frameworks to measure LLM and model quality, accuracy, latency, and cost, and use them to drive continuous improvement
• Build reliable, scalable data pipelines for ingesting, cleaning, transforming, and versioning data for training, fine-tuning, and retrieval
• Train, fine-tune, and deploy machine learning models where they outperform off-the-shelf solutions
• Own MLOps end to end: model deployment, serving, monitoring, observability, and retraining in production
• Rapidly prototype and validate new ideas, then harden the successful ones into production-grade systems
• Apply strong software engineering practices, including clean and tested code, CI/CD pipelines, and thorough code reviews
• Use AI coding tools and agents throughout design, development, testing, debugging, and code review
• Integrate with AI agents, MCP servers, and third-party model APIs and platforms
• Drive technical decisions and set best practices for building AI systems that are reliable, secure, and safe (guardrails, privacy, responsible AI)

Qualifications

• 6+ years of hands-on ML & AI engineering experience, including 2+ years building and shipping ML or LLM-powered systems in production
• Strong proficiency in Python and solid software engineering fundamentals (system design, API design, testing, debugging)
• Hands-on experience building LLM applications using commercial APIs (e.g., Anthropic, OpenAI, Google) and/or open-source models
• Experience building AI agents and tool integrations, including designing, building, and deploying MCP (Model Context Protocol) servers and clients
• Proven experience designing and delivering RAG systems, with working knowledge of embeddings, vector databases (e.g., pgvector, Pinecone, Weaviate), and retrieval techniques
• Experience designing LLM evaluations, including curated test sets, automated and LLM-as-judge evals, and regression tracking
• Solid understanding of ML fundamentals, with experience training, fine-tuning, and deploying models using frameworks such as PyTorch, scikit-learn, or Hugging Face
• Strong experience building data pipelines and working with SQL/PostgreSQL, plus tools such as Airflow, dbt, or Spark
• Hands-on MLOps experience: model serving, experiment tracking (e.g., MLflow, Weights & Biases), monitoring, Docker, and Kubernetes
• Experience with cloud platforms, ideally GCP (Vertex AI, GKE, BigQuery)
• Experience setting up and maintaining CI/CD pipelines (e.g., GitHub Actions, GitLab CI)
• A strong product mindset: you care whether the AI actually solves user problems, not just how it scores on model metrics
• Experience building products from 0→1, ideally in a startup or fast-moving environment
• Ability to navigate ambiguity, prototype quickly, and independently deliver high-quality production systems

Benefits and Culture

• Highly competitive salary and equity package
• Hybrid work environment (3 days on‑site per week), and vacation policy
• Comprehensive health benefits
• Professional development budget, conference attendance and access to AI research resources.
• AIBound is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.