Sr. Engineer - AI

Responsibilities:

• Design and develop agent orchestration frameworks and execution engines supporting complex AI-driven workflows.
• Design and optimise low-latency services supporting real-time conversational and voice interactions.
• Build reusable platform services, SDKs, APIs, and libraries that accelerate development of conversational AI applications.
• Design and implement Model Context Protocol (MCP) integrations and frameworks that enable secure, scalable access to enterprise tools, APIs, data sources, and agent capabilities.
• Develop reusable tool-calling, context-sharing, and interoperability services that support MCP-enabled agent ecosystems.
• Develop runtime services for text- and voice-based agent interactions, including context management, tool execution, memory, and state orchestration.
• Architect microservices and event-driven systems that integrate AI reasoning with enterprise systems such as customer, billing, network, and support platforms.
• Design mechanisms that safely translate model outputs into deterministic business actions and workflows.
• Collaborate with AI/ML engineers to integrate foundation models, retrieval systems, evaluation frameworks, and agent capabilities into production platforms.
• Drive platform reliability, scalability, observability, and performance across high-volume AI workloads.
• Establish software architecture standards, engineering best practices, and reusable design patterns for agentic systems.
• Contribute to technical strategy and platform evolution by evaluating emerging AI, orchestration, and distributed systems technologies.
• Mentor engineers and promote engineering excellence through design reviews, documentation, and knowledge sharing.

Requirements:

• 7+ years of software engineering experience building distributed systems and cloud-native applications.
• Strong proficiency in Python or Java, with working knowledge of both preferred.
• Experience designing APIs, microservices, workflow engines, or platform services.
• Experience building systems that integrate with LLMs, AI services, or conversational platforms.
• Experience designing highly available distributed systems and service-oriented architectures.
• Strong understanding of software architecture, system design, scalability, resiliency, and performance optimisation.
• Demonstrated experience leading technical design discussions, driving architectural decisions, and influencing engineering direction across teams.
• Experience with cloud-native technologies, including containers, Kubernetes, and service-based architectures.
• Excellent problem-solving, debugging, and technical communication skills.
• Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related field, or equivalent practical experience.
• Experience integrating AI systems with enterprise applications, APIs, tools, or data platforms through standardised integration patterns.

Must Have Skills:

• Python, Rust, or Go software engineering.
• Distributed systems, APIs, and microservices.
• LLM, AI service, and conversational platform integration.
• Cloud-native architecture, containers, and Kubernetes.
• Software architecture and technical leadership.

Nice-to-Have:

• Experience building agent orchestration frameworks, workflow platforms, or AI execution systems.
• Experience with conversational AI, voice platforms, speech technologies, or real-time interaction systems.
• Familiarity with retrieval systems, vector databases, memory architectures, and evaluation frameworks.
• Experience designing internal developer platforms, SDKs, or reusable engineering frameworks.
• Knowledge of event-driven architectures, distributed workflows, and enterprise integration patterns.
• Experience with Model Context Protocol (MCP), agent tool-calling frameworks, or similar standards for connecting AI systems with external tools and services.
• Experience designing reusable tool integration frameworks, context management systems, or agent interoperability platforms.