Our client is a global cybersecurity company focused on protecting millions of users and organizations through advanced digital security, privacy, and identity solutions. Their products help safeguard personal data, devices, and online interactions across a rapidly evolving digital landscape.
We’re hiring an experienced Senior Platform/AI engineer to join a growing AI engineering practice. In this role, you will design, deliver, and scale intelligent systems that drive real business outcomes. You will architect production-grade AI solutions spanning agentic workflows, retrieval-augmented pipelines, and LLM-integrated applications that enable enterprise automation at scale.
This role requires both deep technical expertise and strong delivery ownership, working across the full stack to take AI initiatives from whiteboard to production with speed and precision.
The team works on designing, building, and delivering enterprise AI solutions used by multiple departments across the company.
Key tasks include:
Designing AI architectures and workflows before implementation, including defining trigger points, process flows, and system interactions
Building AI-driven solutions such as automated workflows, AI agents, and LLM-based applications
Implementing AI pipelines and integrations using tools such as LLM APIs, Python-based logic, and orchestration platforms
Developing systems that combine different data sources (e.g., files, databases, or enterprise systems) and automate business processes
Identifying potential risks such as security concerns, failure scenarios, and the need for human-in-the-loop review
Continuously improving AI workflows by selecting the most appropriate technologies and development approaches
Delivering solutions incrementally in smaller phases so business teams can start using them quickly and provide feedback
Solid full-stack or backend development background with advanced Python skills, including concurrency, async patterns, and performance-aware design
Familiarity with cloud platforms (AWS, Azure, GCP) and modern DevOps tooling including Docker, Kubernetes, and CI/CD pipelines
3–7+ years of hands‑on experience in software, AI, or ML engineering with a track record of shipping production‑grade systems
Practical experience working with Large Language Models (LLMs), including navigating API cost trade‑offs and managing unpredictable model behaviours in production
Experience enforcing structured LLM outputs and building reliable pipelines using orchestration frameworks such as LangChain or LangGraph
Clear, confident communicator able to translate complex AI concepts for both technical peers and non‑technical stakeholders
Strong grasp of Generative AI fundamentals and how they differ from conventional software development, particularly when solving open‑ended or creative problems
Proficiency in prompt engineering techniques including multi‑shot prompting, persona and tone setting, and structuring effective context and constraint
Proven ability to lead technical workstreams, mentor junior engineers, and translate ambiguous business needs into well‑scoped engineering solutions
Nice to have
Demonstrated ability to optimize AI workflows through systematic benchmarking, bottleneck analysis, and decomposing complex LLM call chains into efficient, maintainable components
Practical judgment on when agentic architectures are appropriate including an honest assessment of their complexity, failure modes, and operational overhead
Expertise in Retrieval‑Augmented Generation (RAG): grounding model responses in authoritative knowledge bases and surfacing cited, verifiable claims
Skilled in implementing input/output guardrails to defend against prompt injection, hallucinations, and harmful or policy‑violating content