AI Engineer

Job Details:

Must Have Skills
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

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) 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)

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.
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.
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
o Not automation-only - intelligent reasoning systems not scripts

Thanks & Regards QUALIS1 INC 2500 Wilcrest Dr. Suite 300 Houston TX 77042

Vinayak Vashisth Senior IT Recruiter