Senior AI Engineer

Overview

Position Summary

Key Responsibilities

• Design, develop, and optimise AI agent-based systems for enterprise applications, including both single-agent and multi-agent architectures.
• Build intelligent agent frameworks leveraging LLMs for reasoning, planning, decision-making, and task execution.
• Implement advanced capabilities such as tool usage, memory systems, reflection mechanisms, and feedback loops.
• Architect and maintain tool-augmented AI systems capable of interacting with APIs, databases, search services, and external platforms.
• Develop scalable pipelines and infrastructure supporting model orchestration, monitoring, evaluation, and operational performance.
• Apply modern context-management approaches to support reliable information flow, state persistence, and long-horizon task execution.
• Evaluate LLM behaviour, latency, context limitations, and cost-performance trade-offs to inform architectural decisions.
• Collaborate closely with engineering, product, and operational stakeholders to translate complex requirements into scalable AI solutions.
• Stay informed on developments in AI agents, reasoning systems, LLM ecosystems, and AI infrastructure technologies.

Candidate Profile

• The ideal candidate will:
• Have hands-on experience developing AI agent or LLM-powered systems beyond basic conversational interfaces.
• Demonstrate expertise in designing agent architectures involving planning, orchestration, memory handling, tool integration, and multi-agent coordination.
• Possess a strong understanding of LLM capabilities, limitations, inference behaviour, and system-level trade-offs.
• Have experience working with modern AI orchestration frameworks or equivalent in-house systems.
• Demonstrate strong analytical and problem-solving skills within complex or ambiguous environments.
• Be comfortable building scalable, reliable, and production-ready AI systems.

Preferred Qualifications

• Master’s degree or Ph.D. in Computer Science, Artificial Intelligence, Data Science, Statistics, or a related discipline.
• Experience with technologies such as retrieval-augmented generation (RAG), AI agents, and agentic reasoning systems.
• Strong understanding of natural language processing (NLP) concepts and methodologies.
• Experience applying AI solutions within regulated or data-intensive industries would be advantageous.
• Contributions to open-source projects, technical publications, or research initiatives would be highly regarded.