Job Opening AI Engineer

1. Build AI-Native Capabilities - Design and implement agentic workflows and multi-agent systems that solve real business problems across operations, service health, and enterprise workflows. Develop LLM-powered features using APIs (OpenAI, Google, AWS, Anthropic, etc.) with patterns such as RAG, tool use, planning, and memory. Translate business problems into composable AI capabilities, not one-off solutions.
2. Contribute to the AI Delivery Platform (AIDLC) - Build reusable components across platform layers including prompt orchestration, agent frameworks, tooling/API integration layers, evaluation, guardrails, and observability. Help define and standardize AI development patterns, templates, and accelerators. Enable other engineers to build AI features through platform-first abstractions.
3. Deliver End-to-End AI Features - Own delivery from concept prototype production. Implement closed-loop workflows (detect reason act validate). Integrate with enterprise systems via APIs, event streams, and observability platforms.
4. Operationalize AI at Scale - Implement evaluation frameworks (quality, latency, cost, safety). Build monitoring, logging, and feedback loops for AI systems. Ensure solutions meet enterprise standards for governance, auditability, and reliability.

Experience with agent and LLM ecosystem tools Google Agent Development Kit (ADK), LangChain & LangGraph (agent orchestration), Model Context Protocol (MCP) FastMCP or similar connector development, A2A ACP interagent communication protocols Proficiency with LLM streaming APIs Vertex AI Gemini, AWS Bedrock, OpenAI Familiarity with OASF (Open Agentic Schema Framework) agent schema and registry patterns Nice to have skills, Hands-on production experience building LLM-powered applications or agentic systems - this is not a traditional ML/data science role (no model training, no heavy ML pipelines) Strong understanding of multi-agent orchestration, tool-using agents, Retrieval-Augmented Generation (RAG), structured outputs, function calling, and Human-ON-the-Loop (HOTL) workflows Experience with agent and LLM ecosystem tools: Google Agent Development Kit (ADK), LangChain & LangGraph (agent orchestration), Model Context Protocol (MCP) - FastMCP or similar connector development, A2A / ACP inter-agent communication protocols Proficiency with LLM streaming APIs: Vertex AI / Gemini, AWS Bedrock, OpenAI Familiarity with OASF (Open Agentic Schema Framework) - agent schema and registry patterns Core Languages & Frameworks Python - strong production experience (primary language required) TypeScript / JavaScript - good to have APIs, Services & Integration FastAPI / AsyncIO - REST API design, webhooks, event-driven services OpenAPI / AsyncAPI / Protobuf - API contract design Apache Kafka, GCP Pub/Sub - event streaming and async agent communication Testing & Quality Engineering Automated Test-Driven Development (TDD) - designing systems with test-first discipline Regression testing - ensuring behavioral stability across rapid iterations End-to-End (E2E) testing - validating agent workflows across services and integrations Test automation for APIs, agents, and event-driven systems Platform, Infrastructure & Cloud Experience working in cloud environments (GCP preferred, AWS) Kubernetes; Google Cloud Run / Cloud Run Jobs - hands-on operational depth Docker containerization GitHub Actions, Cloud Build - CI/CD pipelines Familiarity with microservices, distributed systems, and Infrastructure-as-Code (Terraform, etc.) Data & Storage VectorDB - retrieval systems for RAG and knowledge grounding Firestore, MongoDB, or equivalent NoSQL PostgreSQL / SQL - relational databases Google Cloud Storage (GCS) - artifact and deployment package management Redis - caching Observability & Reliability OpenTelemetry - tracing, spans, structured observability Grafana - dashboards and SLO visualization DORA metrics & SLO engineering Security, Identity & Governance Open Policy Agent (OPA) - policy enforcement in agent workflows SPIFFE / Workload Identity - non-human identity and mTLS Mindset & Work Style Genuinely hands-on, strategic AI-first mindset engineer who takes full ownership of work Thrives in a fast-paced environment with continuous experimentation Actively leverages modern AI-assisted development tools - GitHub Copilot, Codex, and Claude Track record of shipping production-grade systems, not prototypes Comfortable with ambiguity and rapid evolution of AI tooling Preferred Skills and Attributes Experience with prompt/version management and evaluation tooling (2+ years) Skills generation and agent builder experience Familiarity with emerging agent frameworks and orchestration patterns Understanding of AI observability and evaluation frameworks (quality, latency, cost, safety) Experience with Responsible AI practices (guardrails, safety, auditability) Knowledge of cost/performance tradeoffs in LLM systems Experience building monitoring, logging, and feedback loops for AI systems Mentoring experience - ability to guide engineers on AI-first development approaches Experience contributing to platform-first abstractions that enable other engineers to build AI features Familiarity with closed-loop workflows (detect reason act validate), 5. Drive AI Engineering Excellence - Apply modern best practices in prompt engineering and versioning, agent orchestration and tool use, retrieval strategies and knowledge grounding. Mentor engineers on AI-first development approaches. Contribute to a culture of rapid experimentation and measurable delivery. Prior Experience, Industry Background, or Domain Expertise 5+ years in software/platform engineering with a strong delivery focus Prior experience building production-grade LLM-powered applications or agentic systems (not experimental/prototype-only) Background in enterprise platform engineering, cloud-native development, or distributed systems Experience with healthcare, insurance, or regulated industry environments is a plus Familiarity with enterprise AI delivery lifecycle concepts - governed, scalable, auditable AI systems Understanding that this role is fundamentally different from traditional roles: o Not a data scientist - no model training, no heavy ML pipelines o Not a one-off builder - contributing to a shared platform o Not experimental-only - production delivery at scale

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