AI Technical Lead

You will be responsible for taking AI from prototype production, integrating it into high-scale data systems and customer- facing use cases.
The candidate will have responsibilities across the following functions:
AI Systems and Agent Development:
Design and build AI agents for automating security, data engineering, and observability workflows.
Develop LLM-driven applications, including chatbot integrations and intelligent automation systems.
Implement RAG (Retrieval-Augmented Generation) pipelines using vector databases.
Build systems for Prompt orchestration, Context and memory management and Tool usage and agent workflows.
AI Platform and Model Engineering:
Fine-tune and optimise LLMs for domain-specific use cases (security, observability, IoT/OT).
Work with transformer models, embeddings, and retrieval systems.
Experiment with and implement Generative AI architectures and Reinforcement learning techniques (RLHF).
Build knowledge-driven AI systems using vector search and knowledge graphs.
Production Engineering and Deployment:
Develop APIs and backend services for AI inference and orchestration.
Integrate AI systems into data pipelines, connectors, and streaming architectures.
Deploy and scale AI workloads using Docker, Kubernetes, and cloud platforms (AWS / Azure / GCP).
Optimise systems for Latency, throughput, and Cost efficiency.
POC and Customer-Focused Development:
Work closely with POC teams and customers to build AI-driven solutions.
Translate real-world use cases into production-ready AI workflows.
Rapidly prototype and iterate on AI features and agents.
Ensure solutions are scalable and aligned with platform architecture.
Model Performance and Optimisation:
Improve model performance in terms of Accuracy, Context awareness and Domain adaptation.
Evaluate and benchmark models and pipelines.
Monitor and optimise inference performance and system reliability.
Collaboration and Integration:
Collaborate with Platform engineering teams for system integration, Product teams for feature definition and Customers and POC teams for real-world validation.
Bridge the gap between AI research and production engineering.
Key Outcomes / KPIs:
Production deployment of AI agents and workflows.
Reduction in manual effort through automation.
Improvement in model accuracy and system performance.
Faster POC-to-production turnaround.
Adoption of AI features by customers.
Performance metrics (latency, throughput, cost efficiency).
Requirements:
Strong programming skills in Python (mandatory); Go is a plus.
Hands-on experience with LLM integration, prompt engineering, RAG architectures and retrieval systems.
Experience with vector databases like Weaviate, Pinecone, FAISS, or similar.
Familiarity with AI frameworks such as LangChain, LangGraph, LlamaIndex, Haystack, or similar.
Experience with LLM fine-tuning and optimisation and Transformer models and embeddings.
Understanding of Generative AI architectures and Reinforcement learning (RLHF) (preferred).
Experience with PyTorch or TensorFlow.
Experience building and deploying systems using Docker, Kubernetes, AWS / Azure / GCP.
Strong understanding of APIs and backend systems, distributed systems and data pipelines.
Experience in Cybersecurity, observability, or data platforms.
Experience in High-scale data processing or streaming systems.
Experience building AI agents or autonomous workflows.
Ability to take solutions from prototype to production.

Experience
8-12 yrs