Senior Generative AI Engineer

Enterprise AI Platform Engineer

We are building enterprise-grade AI platforms for observability, evaluation, command and control, and safeguards across generative and agentic AI systems deployed in complex, regulated environments.

In this role you will design, integrate, and operate core AI platform capabilities ensuring that intelligent systems run safely, reliably, and in full alignment with enterprise expectations for security, auditability, resiliency, and operational excellence. Your work will span agent tracing, evaluation pipelines, guardrails and intervention services, registry and governance tooling, and operational control experiences.

In This Role, You Will

• Design and build production multi-agent systems, coordinating specialized agents through orchestrator patterns with clearly defined tool-use protocols and inter-agent communication
• Implement Model Context Protocol (MCP) servers to connect AI agents with external tools, data sources, APIs, and enterprise services
• Build and operate RAG pipelines and knowledge graph-backed retrieval systems, leveraging vector and graph databases to ground agent reasoning in accurate, contextual data
• Develop LLM-powered analysis capabilities — semantic understanding of logs, code, and configurations — to drive intelligent automation within multi-step agent workflows
• Design and operate distributed observability systems using OpenTelemetry, building self-healing automation that detects anomalies, performs root-cause analysis, and triggers autonomous remediation
• Build event-driven agent pipelines using Kafka and message queue systems, ensuring reliable and ordered processing across distributed agent components
• Implement agent governance controls including safety guardrails, approval workflows, blast-radius limits, and audit logging to ensure agents operate within defined boundaries
• Integrate AI-powered quality and compliance gates into CI/CD pipelines, enabling automated validation at each stage of the delivery lifecycle
• Translate enterprise requirements into modular, maintainable agent architectures and contribute to large-scale agentic AI strategy
• Collaborate with peers, client engineering leads, and program managers to resolve technical challenges, meet delivery targets, and communicate progress clearly
• Lead projects and act as an escalation point, providing mentorship and technical guidance to less experienced engineers
• Maintain strong operational rigor: runbooks, incident response procedures, performance regression gating, and documentation for audit and governance

Required Qualifications

• 4+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, or education
• 2+ years of hands-on experience building and deploying generative AI or agentic AI systems in production environments
• Strong proficiency in Python; experience designing and consuming REST and/or gRPC APIs
• Demonstrated experience with LLM integration, prompt engineering, and tool-use patterns in multi-step AI workflows
• Experience with at least one agentic AI framework (e.g., LangGraph, AutoGen, CrewAI, OpenAI Swarm, Google ADK, or Claude Agent SDK)
• Solid understanding of distributed systems, event-driven architecture, and microservices design
• Experience with cloud-native infrastructure and containerized deployments (Docker, Kubernetes)
• Strong written and verbal communication skills with the ability to document technical designs, present to stakeholders, and produce clear operational artifacts

Desired Qualifications

• Hands-on experience building production agentic AI systems using one or more frameworks such as OpenAI Swarm, AutoGen, CrewAI, LangGraph, Google ADK, or Claude Agent SDK
• Experience implementing Model Context Protocol (MCP) integrations for tool use, context management, and agent-to-agent communication
• Experience designing and implementing RAG (Retrieval-Augmented Generation) pipelines for knowledge-grounded AI applications
• Proficiency with vector databases such as Pinecone, Weaviate, Qdrant, pgvector, Redis Vector DB, or FAISS
• Experience with graph databases, particularly Neo4j, for relationship-aware data modeling and querying
• Hands-on experience with distributed observability and self-healing systems — instrumenting services, detecting anomalies, and triggering automated remediation
• Experience with OpenTelemetry for distributed tracing, metrics, and logging across multi-service architectures
• Experience with event streaming platforms such as Kafka and message queue systems such as RabbitMQ, ZeroMQ, or Redis MQ
• Proficiency in Python (5+ years); REST and/or gRPC; event-driven design patterns
• Experience with identity and access management (OAuth scopes, RBAC/ABAC) and sensitive data handling for enterprise applications

Good to Have

• Experience with containerization and orchestration using Docker and Kubernetes
• Familiarity with sandboxed code execution environments such as Docker, Firecracker, etc