AI Systems Architect / AI Operations Engineering Lead

Job Description Roles & Responsibilities We are building a 100% AI based IT Company that delivers enterprise-grade digital solutions using a blend of autonomous AI agents and expert human oversight. Our operating model covers the full delivery lifecycle — from requirements and architecture to engineering, Design, Development, Code Review, QA, DevOps, security, and client reporting. We currently leverage best-in-class commercial AI platforms for speed and quality, with a strategic roadmap to transition core workloads to self-hosted, offline-capable models on owned infrastructure for better cost, privacy, and control. This role is for senior builders who has real experience designing production-grade AI systems, agent workflows, and enterprise software delivery platforms. The selected person will help design how AI agents collaborate across the full lifecycle of enterprise solutions, while keeping strong human approval layers for security, architecture, client commitments, and production release management. • Design an end-to-end multi-agent operating model for enterprise IT delivery, from requirements gathering to deployment and support. • Build orchestration between role-based agents such as project manager, business analyst, solution architect, frontend, backend, QA, mobile, DevOps, cloud, and cybersecurity agents. • Define approval workflows, escalation rules, and audit trails for high-risk outputs. • Evaluate paid AI platforms for fast launch and create a roadmap for self-hosted model adoption. • Build secure deployment, monitoring, cost tracking, evaluation, and governance systems for AI-assisted delivery. Desired Candidate Profile Experience in software engineering, cloud architecture, platform engineering, AI systems, or a related enterprise technology role. Strong hands-on experience with LLM applications, AI agents, workflow orchestration, APIs, and production deployment. Experience in at least three of these areas: frontend, backend, DevOps, cloud, QA automation, cybersecurity, mobile, or solution architecture. Strong judgment on when AI can be autonomous and when human review must be mandatory. Preferred tools experience Agent frameworks: LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel. Model serving and local AI: Ollama, vLLM, TGI, TensorRT-LLM, CUDA, Llama, Mistral, Qwen, DeepSeek. Dev and cloud stack: Docker, Kubernetes, Terraform, Ansible, GitHub, GitLab, AWS, Azure, GCP, Cloudflare. App stack: Python, FastAPI, Django, Node.js, NestJS, Go, Java, .NET, React, Next.js, Flutter, React Native. QA and security: Playwright, Cypress, Selenium, Postman, Snyk, Semgrep, SonarQube, OWASP ZAP. Observability and data: Prometheus, Grafana, ELK, Datadog, PostgreSQL, Redis, Elasticsearch, Kafka, pgvector, Milvus, Weaviate. Hardware awareness expected - Candidates should understand how to plan or operate infrastructure such as NVIDIA H100, H200, A100, L40S, RTX 4090, or RTX 6000 Ada class GPUs, along with AMD EPYC or Intel Xeon server environments, high-memory systems, NVMe storage, and high-bandwidth networking suitable for local inference and agent operations. Employment Type Full Time Company Industry IT - Software Services Department / Functional Area Other Keywords AutomationAI Systems ArchitectAI Technical LeadMachine LearningHead Of AI EngineeringAI Solutions ArchitectMLOpsAI Operations ManagerAI Infrastructure LeadModel DeploymentAI Platform ArchitectMachine Learning Systems Architect Get real-time job updates only on our App