Responsibilities
- Develop and deploy enterprise-scale AI systems, including agent-based architectures, memory-enhanced models, LLM-integrated features, and automated intelligence workflows.
- Design cloud-hosted AI platforms on major providers (AWS, Azure, GCP) with strong DevOps and LLMOps foundations to ensure performance and maintainability.
- Embed artificial intelligence functions into legacy enterprise systems such as CRM, ERP, loan processing, and insurance claims platforms using scalable APIs and intelligent data flows.
- Guide technical execution of AI initiatives within multidisciplinary teams, collaborating with designers, strategy leads, and client representatives to turn advanced AI concepts into measurable business value.
- Enforce ethical AI standards by conducting adversarial testing, bias detection, automated evaluation, and incorporating human oversight mechanisms prior to deployment.
- Shape internal blueprints and architectural templates for recurring use cases such as context-aware pipelines, autonomous agents, LLM integrations, and model validation frameworks.
- Help establish and refine engineering best practices specific to AI development, including code standards, testing methodologies, prompt evaluation protocols, and definitions of production readiness.
- Create modular tools, reusable components, and functional 'skills' that accelerate future project delivery and improve system consistency.
- Engage in model benchmarking, cost-efficiency analysis, and system design reviews to maintain technical leadership and innovation.
- Disseminate expertise through technical documentation, internal presentations, and direct mentorship across the engineering organization
Compensation
Competitive salary and benefits package
Work Arrangement
Hybrid or remote options available
Team
AI Labs team focused on delivering advanced AI solutions for enterprise clients
Responsibilities
- Design and build production-grade AI solutions including agentic systems, memory systems, LLM-powered features, and intelligent automation for enterprise clients.
- Architect cloud-native AI systems on AWS, Azure, or GCP with robust DevOps/LLMOps pipelines for scalability and reliability.
- Integrate AI capabilities into existing enterprise platforms (CRM, ERP, lending systems, claims platforms) through well-designed APIs and data pipelines (leveraging AI of course).
- Lead AI technical delivery within cross-functional squads alongside designers, strategists, and client stakeholders — translating complex AI concepts into high-impact outcomes.
- Implement responsible AI practices including red-teaming, bias testing, evaluation harnesses, and human-in-the-loop design before anything ships.
- Contribute to reference architectures for common delivery patterns: knowledge/context pipelines, agentic systems, LLM integrations, and evaluation frameworks.
- Help define and evolve engineering standards for AI Labs work — code quality, testing approaches, prompt evaluation frameworks, and what “production-grade” means in practice.
- Build reusable tooling, accelerators, and “skills” that make the next project faster and more reliable.
- Participate in model evaluation, cost optimisation, and architecture reviews that sharpen technical edge.
- Share knowledge across the engineering team through documentation, tech talks, and hands-on mentoring.
Available for qualified candidates
