Generative AI Engineer

AI Engineering & Agentic Systems Eden Prairie, MN Experience: 10+ years Job Details: Must Have Skills Experience with agent and LLM ecosystem tools Google Agent Development Kit (ADK), Lang Chain & Lang Graph (agent orchestration), Model Context Protocol (MCP) Fast MCP or similar connector development, A2A ACP interagent communication protocols Proficiency with LLM streaming APIs Vertex AI Gemini, AWS Bedrock, Open AI Familiarity with OASF (Open Agentic Schema Framework) agent schema and registry patterns Nice to have skills Detailed Job Description 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), Lang Chain & Lang Graph (agent orchestration), Model Context Protocol (MCP) — Fast MCP or similar connector development, A2A / ACP inter-agent communication protocols • Proficiency with LLM streaming APIs: Vertex AI / Gemini, AWS Bedrock, Open AI • 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 • Fast API / AsyncIO — REST API design, webhooks, event-driven services • Open API / Async API / Proto buf — 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 • Vector DB — retrieval systems for RAG and knowledge grounding • Fire store, MongoDB, or equivalent NoSQL • PostgreSQL / SQL — relational databases • Google Cloud Storage (GCS) — artifact and deployment package management • Redis — caching Observability & Reliability • Open Telemetry — 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) Primary Responsibilities 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. 1. 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. 1. 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. 1. 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. 1. Drive AI Engineering Excellence — Apply modern best practices in prompt engineering and versioning, agent orchestration and tool use, retrieval strategies and knowledge grounding. 5. 5. 5. 6. 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 o Not automation-only — intelligent, reasoning systems, not scripts AI Skills All contractor resources are expected to demonstrate baseline proficiency in enterprise-approved AI tools as part of their day-to-day responsibilities. This includes, but is not limited to: • Consistent Use: Maintain a minimum of 90% weekly usage of AI tools such as GitHub Copilot, Microsoft 365 Copilot, and other GenAI platforms approved by the enterprise. • Applied Productivity: Leverage AI tools to enhance coding, documentation, data analysis, and decision-making workflows. • Continuous Learning: Stay current with evolving AI capabilities and features, and apply them to improve delivery quality and velocity. We are seeking candidates who actively leverage GitHub Copilot, Codex, and Claude as core development tools — not as optional add-ons. AI-assisted development is a baseline expectation, not a differentiator. Optional Fields: • AI Delivery Lifecycle Platform (AIDLC) — An enterprise platform enabling rapid, scalable, and governed delivery of AI-powered capabilities across the organization. Provides standardized patterns for agent orchestration, LLM integration, evaluation, observability, and lifecycle management. • Agentic Workflow Development — Multi-agent systems and intelligent workflows for operations, service health, and enterprise use cases • Platform Reusable Component Library — Prompt orchestration frameworks, tooling/API integration layers, evaluation and guardrails, observability pipelines • Please share any additional information that would help vendors better understand the role, candidate profile, or hiring priorities. We are seeking a genuinely hands-on, strategic AI-First Mindset Engineer who takes full ownership of work, thrives in a fast-paced environment, and continuously experiments. Please ensure candidates are thoroughly vetted against these expectations: • Candidates must demonstrate real production experience with agentic AI systems — not just academic or prototype exposure • Look for evidence of shipping production-grade systems end-to-end (concept → deploy → operate) • Verify active, daily use of AI-assisted development tools (GitHub Copilot, Codex, Claude) • This is a platform engineering role — candidates should show experience building for reuse and scale, not one-off solutions • Strong preference for candidates who think in AI capabilities and workflows, not just code • Candidates should balance experimentation with production discipline and be focused on business impact, not just technical novelty Technical Recruiter VMC Soft Technologies inc. EMail: recruiterbang@vmcsofttech.com Ph No: 602-610-2169 Ext: 202