About Giggso At Giggso, we bridge the gap between high-level AI strategy and code-level execution. We build context-aware, secure enterprise AI engineering solutions across core Business Operations (Sales, Support, Rev Ops) 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 high-ownership, hands-on AI / LLM Engineering Tech Lead to drive the technical execution of our core AI platforms. In this role, you will bridge deep architectural vision with direct code-level execution. You will lead an agile engineering pod building enterprise-grade Agentic Workflows, Knowledge Graphs, and AI Security Safeguards.

If you are driven by passion and innovation, determined to bend the limits to build something truly transformative — this role is for you.

Key Responsibilities Architectural Leadership &

- Pod Execution Lead an engineering pod (AI/ML Engineers, Full-Stack Developers, and Dev Ops) to ship low-latency, production-ready AI features. Translate high-level blueprints into actionable technical specifications, clean codebases, and sprint backlogs. Enforce engineering excellence through code reviews, automated CI/CD testing protocols, and robust error-handling standards. Hands-On Agentic &
- Knowledge Systems Development Architect &
- Code: Build multi-modal LLM workflows and autonomous agentic systems using modern orchestration frameworks.

Knowledge Layer Integration: Implement knowledge graphs,



dynamic ontologies, and advanced vector retrieval strategies (Hybrid Search, Graph RAG, Re-ranking) that go beyond standard naive RAG. AI Security &

- Guardrails: Deploy active safeguards against prompt injection, model jailbreaks, hallucination, and data leakage using core AI Security principles. Production MLOps, Eval &
- Performance LLM Ops: Build automated pipelines for continuous model evaluation (e.g., RAGAS, Tru Lens), dynamic prompt versioning, and latency tracking. Cost &
- Throughput Optimization: Optimize token consumption, context window management, caching, and model inference costs across multi-cloud deployments.

Observability: Monitor model drift, data distribution shifts, and edge-case execution in live enterprise production environments. Cross-Functional Execution Collaborate closely with Product Managers, Solution Architects, and client teams to resolve complex edge cases and accelerate feature delivery. Serve as a technical mentor, elevating team execution standards and unblocking complex algorithmic or system challenges daily.

Required Qualifications Education &

Experience: Experience: 8+ years of core software engineering experience, including 3+ years specifically architecting and delivering AI/ML or LLM-based products into production.

Leadership: Proven track record leading agile pods, conducting technical design reviews,



and mentoring developers.

Education: Master’s in Computer Science, Data Science, AI, or equivalent practical experience demonstrated through shipped products or open-source contributions and skilled certifications.

Technical Stack Requirements: Languages &

- Core CS: Strong mastery of Python (Fast API, Py Dantic, Asyncio) with familiarity in Type Script, Go, or Java.

Agentic

Frameworks &

- AI Stack: Hands-on experience with modern LLM orchestration tools (Lang Graph, Auto Gen, Crew AI, Lang Chain, Llama Index), Py Torch, Hugging Face, and major LLM Provider APIs.

Vector

Engines &

- Knowledge Graphs: Direct working experience with vector databases (Qdrant, Pinecone, Milvus, Weaviate) and Knowledge Graph technologies (Neo4j, RDF/Ontologies). AI Security &
- Guardrails: Familiarity with adversarial prompt testing, red teaming concepts, and guardrail implementation. MLOps &
- Infra: Practical experience with Docker, Kubernetes, Git Hub Actions, MLflow, Weights &
- Biases, and serverless AI infrastructure on AWS/GCP/Azure.

Soft Skills: Strong technical articulation and communication skills to engage with technical stakeholders, understand requirements, and present engineering solutions cleanly.

Why Join

Giggso?Pioneer Enterprise AI: Work on cutting-edge Knowledge Graph (Graph RAG) and Agentic tech stacks that solve real business problems. High Impact &

- Ownership: Own features end-to-end—from initial prototype to enterprise deployment.

Culture of Innovation: Collaborate with a team building high-trust AI engineering frameworks and production security platforms. Competitive package and flexible work culture

📌 Ai/ml Tech Lead (Tamil Nadu)
🏢 Giggso
📍 Tamil Nadu