Senior AI Engineer

This is an applied engineering role, not research. You will architect and deliver agentic systems, model-driven workflows, and AI-enabled automation that meet enterprise standards for reliability, performance, cost efficiency, and safety.

Beyond execution, you define how AI is built, establishing implementation patterns, quality standards, and technical guardrails that scale across teams. You operate effectively in ambiguity, make high-leverage architectural decisions, and own AI initiatives end-to-end.

Key Responsibilities

• AI System Architecture & Engineering
• Design, build, and operate production AI-enabled systems solving complex product and business challenges.
• Architect agentic systems with autonomous tool use, multi-step reasoning, and controlled execution.
• Define and enforce Model Context Protocol (MCP) patterns for tools, memory, policies, and system boundaries.
• Make informed model selection decisions, balancing quality, latency, cost, and reliability.
• Treat prompts, context, and agent flows as versioned, testable, and observable system components.

Technical Leadership

• Lead high-impact AI initiatives from architecture through delivery and long-term ownership.
• Decompose ambiguous problems into clear technical strategies.
• Identify failure modes, risks, and trade-offs early and design safeguards.
• Influence cross-team direction through technical judgment and example.
• AI Engineering Standards

• Define patterns for:

• Agent behaviour and autonomy
• Prompt and context design
• Evaluation and regression detection

• Safe, explainable AI in production

• Embed AI-assisted development practices across teams (code generation, testing, debugging, documentation).
• Ensure AI becomes a default engineering capability—not an experiment.
• Quality, Reliability & Governance
• Implement monitoring, evaluation, and safeguards for production AI systems, including:
• Hallucination and error detection
• Behavioural regression and drift
• Traceability and explainability
• Own system performance, cost efficiency, security, and compliance.
• Maintain long-term operational health beyond initial delivery.
• Mentorship & Collaboration
• Mentor engineers through design reviews, pairing, and architectural guidance.
• Raise AI fluency and system thinking across teams.
• Partner with Product and Design to translate intent into scalable, maintainable AI solutions.
• Clearly communicate AI trade-offs to technical and non-technical stakeholders.

Qualifications

• Required

• Python, C#, MongoDB, SQL Server, OpenSearch.
• Experience between 5 - 10 Years in Software Industry
• Extensive experience building and operating complex production systems.
• Deep hands-on experience with agentic AI systems in production (autonomous tools, multi-step workflows).
• Strong expertise in Model Context Protocols (MCPs) or equivalent frameworks.

• Strong understanding of:

• Model capabilities and trade-offs (latency, cost, reasoning quality)
• Context window strategies
• Prompt design and system boundaries

• Experience with both frontier and open-source models.

• Proven ability to scale AI systems (latency optimisation, caching, parallelisation, cost control).
• Demonstrated track record of moving AI systems from prototype to sustained production.

• Strong judgment and ability to operate in ambiguous environments.

• Preferred

• Experience in defining engineering standards adopted across teams.
• Experience shaping AI platforms or shared agentic frameworks.
• Background in cloud-native, SaaS, or enterprise environments.
• Experience in regulated or mission-critical systems.