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.