About Giggso:
At Giggso, we bridge the gap between high-level AI strategy and enterprise-grade execution. We build context-aware, secure enterprise AI engineering platforms across core Business Operations (Sales, Support, RevOps) and AI Security Operations.
Moving far beyond basic RAG and static prompts, Giggso builds foundational Data & Knowledge Layers—turning raw unstructured data and enterprise ontologies into trustworthy, audit-ready AI agents. From multi-modal agentic architectures to proactive AI red teaming and security guardrails, Giggso ensures enterprise AI operates reliably at scale.
Role Overview:
We are seeking a visionary and hands-on Director of AI/ML Architecture to lead Giggso’s overall AI technical strategy, system architecture, and machine learning governance. In this executive-track role, you will bridge executive leadership and engineering execution—setting the strategic blueprint for Giggso’s AI platforms, driving architectural standards, and advancing innovations in multi-agent orchestration, Knowledge Graphs, and AI Security Operations.
If you are driven by passion and innovation, determined to bend the limits, and hungry to build something truly transformative that leaves a mark—this role is for you.
Key Responsibilities
Executive Technical Leadership & AI Strategy:
- Define and drive the multi-year AI/ML technical roadmap, aligning platform architecture with long-term business goals, cloud unit economics, and client adoption strategies.
- Partner with executive leadership to evaluate emerging AI paradigms, steer technical investments, and serve as the primary technical authority in high-stakes enterprise client engagements.
- Architect scalable, resilient, and secure AI systems operating across multi-cloud and hybrid environments for both real-time and batch-processing workloads.
Enterprise Platform Architecture & Agentic Systems
- System Design: Spearhead the architectural blueprint for Giggso’s core platform capabilities—advanced Retrieval-Augmented Generation (GraphRAG), Knowledge Graph ontologies, multi-agent frameworks, and model fine-tuning pipelines.
- Optimization & Efficiency: Establish enterprise design patterns for low-latency inference, dynamic prompt caching, model serving, and token cost optimization across high-throughput workloads.
- Technical Prototyping: Remain hands-on through architectural spike execution, technical design reviews, and high-impact code reviews for core framework modules.
AI Security, Governance & MLOps Strategy
- Responsible AI & Security: Champion AI safety, data privacy, model governance, and compliance frameworks (SOC2, GDPR, ISO 42001) while building active defenses against prompt injection, jailbreaking, and data leakage.
- Enterprise MLOps: Standardize the end-to-end MLOps lifecycle—implementing automated continuous evaluation (Evals), prompt versioning, model registries, drift detection, and automated failover systems.
Team Building & Engineering Excellence
- Mentor, manage, and elevate AI/ML Tech Leads, Principal Architects, and Senior Engineers across global pods.
- Foster a high-rigor engineering culture focused on rapid iteration, research-backed development, security and production reliability.
Required Qualifications
- Total Experience: 8+ years of progressive software engineering and system architecture experience with 3+ years in a leadership role delivering production-grade AI systems at scale.
- Commercial Acumen: Proven ability to manage cloud compute budgets, optimize model inference unit economics, and present technical vision to C-suite stakeholders.
Deep Technical Expertise
- Core Languages & Frameworks: Expert proficiency in Python (FastAPI, PyDantic, PyTorch) and enterprise cloud stacks (AWS Bedrock/SageMaker, GCP Vertex AI, Azure OpenAI).
- Agentic Workflows & Knowledge Graphs: Direct architectural experience with multi-agent orchestration (LangGraph, AutoGen, CrewAI, LangChain), vector databases (Qdrant, Pinecone, Milvus, Weaviate), and Knowledge Graphs (Neo4j, RDF/OWL, Ontologies).
- AI Security & Governance: Deep understanding of guardrail implementation, adversarial prompt testing, red-teaming paradigms, and model evaluation protocols (e.g., RAGAS, TruLens).
- Infrastructure & Systems Design: Proven mastery of microservices, event-driven architectures, Kubernetes, Docker, and enterprise MLOps CI/CD pipelines.
Education & Credentials
- Education: Master’s, or Ph.D. in Computer Science, Artificial Intelligence, Data Science, or a related quantitative field (or equivalent practical experience demonstrated through industry accomplishments).
- Certifications (Preferred): Professional cloud or AI certifications (e.g., AWS Certified Machine Learning Specialty, GCP Professional ML Engineer, Azure AI Engineer) or executive level AI program credentials from leading institutions.
Why Join Giggso?
- Shape the Frontier: Architect next-generation Knowledge Graph and Agentic AI stacks that solve fundamental enterprise challenges.
- Executive Impact: Direct visibility with C-suite leadership and freedom to define technical strategy across the enterprise.
- Culture of Innovation: Lead a high-performing engineering organization building trustworthy, audit-ready AI infrastructure.