Senior AI Engineer

We are seeking an experienced and driven Agentic AI Platform Engineer to design and build the core backend infrastructure for our enterprise-grade Agentic AI platform. In this role, you will move beyond prototyping to architect robust services handling agent orchestration, model routing, enterprise security, cost telemetry, and deep data integration. You will be instrumental in ensuring our AI systems are scalable, observable, secure, and seamlessly connected to enterprise data ecosystems.

Responsibilities:

• Core Infrastructure Development: Build and maintain critical backend services for model routing, dynamic agent orchestration, and multi-agent graph protocols.
• Integration and Data Pipelines: Implement and optimise production-grade RAG pipelines (retrieval, embedding/vector search, and grounding) and develop robust enterprise connectors (SQL, Kafka, Salesforce, SharePoint).
• Security and Compliance: Engineer built-in data protection mechanisms, including PII detection/redaction, RBAC/ABAC policy enforcement, and tamper-evident audit logs.
• FinOps and Observability: Instrument complete system telemetry (tracing, metrics, and logs) and design usage/cost tracking mechanisms for multi-provider AI workloads.
• Deployment and Scaling: Design resilient REST APIs and deploy production services onto cloud-managed container orchestrators (Kubernetes/ECS).

Requirements:

• Expert Python and Async Frameworks: Extensive hands-on experience building production services using Fast API or equivalent asynchronous Python frameworks.
• Production AI Systems: Track record of shipping at least one LLM-powered system to production (multi-step orchestration, advanced RAG, or autonomous agents); prototypes or toy apps do not qualify.
• Agent/Graph Frameworks: Practical experience with agent frameworks. LangGraph is highly preferred (LangChain, AutoGen, or comparable frameworks considered).
• Data and Caching: Strong competency running PostgreSQL and Redis in high-throughput production environments.
• Cloud and DevOps: Direct experience deploying and managing production services on Kubernetes (EKS) or AWS ECS.
• Observability: Solid fundamentals in system instrumentation, specifically using Open Telemetry tracing, structured logging, and metrics collection.
• API Design: Proven ability to design secure, clean, and scalable REST APIs integrated with legacy enterprise ecosystems.

Good to Have:

• LLM Gateway Management: Experience using LiteLLM, Kong, Portkey, or Azure APIM for virtual keys, budget tracking, fallback chains, and caching.
• Advanced Protocols: Exposure to Model Context Protocol (MCP) for building or consuming MCP servers/clients, as well as multi-agent protocols.
• Cloud AI Platforms: Hands-on experience with Amazon Bedrock, SageMaker, or cross-provider model routing.
• Compliance Engineering: Knowledge of WORM retention, hash-chained audit logs, and automatic PII masking.
• AI FinOps: Experience setting up cost instrumentation and cost-tracking dashboards for massive LLM workloads.
• Enterprise Middleware: Experience building enterprise data connectors or working with event streaming via Apache Kafka.
• Policy-as-Code: Familiarity with OPA-based policy enforcement and sophisticated RBAC/ABAC security design.