Director

Location: Anywhere in USAJob Title: Hands-on AI Architect – GenAI & Agentic SystemsRole Overview We are seeking a hands-on AI Growth Leader with deep technical expertise in designing, building, and scaling GenAI and agentic AI systems. This role is centered on architecture, engineering execution, and innovation, rather than traditional pre-sales. You will act as a lead AI architect and practitioner, owning the end-to-end lifecycle of intelligent systems - from prototyping and proof-of-concepts to production-grade deployments. Your work will focus on multi-agent systems, LLM orchestration, and enterprise-grade AI platforms, driving real-world impact across automation, decision intelligence, and customer experience. While you will collaborate with business and client stakeholders, your primary value lies in hands-on solutioning, rapid experimentation, and technical leadership. Key Responsibilities 1. Hands-on AI Architecture & Development Design and implement end-to-end GenAI systems, including: Multi-agent architectures (planner-executor models, autonomous agents) RAG pipelines and knowledge-grounded AI systems Tool-augmented LLM workflows (function calling, API orchestration) Build production-ready AI solutions, not just prototypes, ensuring scalability, reliability, and observability. Develop reusable frameworks, accelerators, and reference architectures for enterprise AI adoption. 2. Agentic AI Systems & Advanced AI Patterns Architect and deploy agentic AI solutions with: Memory, reasoning, task decomposition, and self-improvement loops Multi-agent collaboration and orchestration patterns Workflow automation using LLM-driven decision engines Experiment with advanced paradigms such as: Reflection and planning agents Retrieval + reasoning hybrid systems Autonomous pipelines for analytics and operations 3. Deep Technical Ownership of GenAI Stack Work hands-on with: Frameworks: LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI Models: OpenAI, Claude, open-source LLMs (Llama, Mistral, etc.) Vector DBs: Pinecone, Weaviate, FAISS, Azure AI Search Build and optimize: Prompt engineering strategies Fine-tuning and adaptation (LoRA, PEFT where applicable) Latency, cost, and inference optimization Implement evaluation pipelines (hallucination detection, grounding accuracy, guardrails). 4. Cloud-Native AI Engineering Architect and deploy solutions on: Azure OpenAI, AWS Bedrock, Google Vertex AI Build scalable pipelines using: Kubernetes, serverless architectures, API gateways Data pipelines (Airflow, Kubeflow, Spark where needed) Ensure MLOps / LLMOps practices, including: CI/CD for AI systems Model/version lifecycle management Monitoring and feedback loops 5. Rapid Prototyping & Innovation Build POCs, MVPs, and experimental systems rapidly to validate new ideas. Translate ambiguous business problems into working AI solutions quickly. Stay at the cutting edge of: Multimodal AI AI agents and orchestration frameworks Edge AI and lightweight deployments