Bilue is hiring a Senior AI Engineer

The Role We’re looking for an AI Engineer to join The Foundry and work at the intersection of client delivery and internal capability building. This is a hands-on engineering role where you’ll design, build, and ship production-grade AI systems for Bilue’s clients across financial services, government, insurance, and retail — while simultaneously contributing to the reference architectures, tooling, and engineering standards that define how AI Labs operates. This isn’t a research role. It’s not a prompt-engineering role. You’ll be building agentic systems that survive contact with real users, integrating AI into enterprise environments, and setting the quality bar for what “production-grade” means at Bilue. What You’ll Do Client Delivery (The Foundry) - 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. Internal Capability (The AI Foundry) - Contribute to Bilue’s 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 Bilue’s technical edge. - Share knowledge across the engineering team through documentation, tech talks, and hands-on mentoring. Experience & Mindset - Demonstrable experience delivering AI or GenAI solutions in a production environment — not just prototypes or demos. - Strong problem-solving ability and the analytical mindset to work with ambiguous, evolving requirements. - The ability to communicate technical concepts clearly to non-technical stakeholders and client executives. - Genuine systems thought leadership — you understand why the system matters, not just how it works. - A collaborative, low-ego approach, working across teams, clients, and contexts with adaptability. - Comfort operating in a fast moving environment where you’ll work across multiple clients and industries. - Strong opinions, weakly held - curious mind with the ability to rapidly evolve when faced with contradictory evidence. Technical Skills - Strong programming skills in Python; familiarity with TypeScript or Java is a plus. - Hands-on experience with AI and agentic frameworks — LangChain, AgentCore , LlamaIndex, LangGraph, CrewAI, vector databases, or similar. - Working expertise with cloud platforms (AWS, Azure, or GCP) - Solid understanding of data engineering fundamentals — SQL, ETL/ELT, distributed systems, handling large and unstructured datasets. - Experience designing and deploying production AI systems with proper observability, monitoring, and evaluation. - Familiarity with LLMOps practices — prompt versioning, model drift detection, cost monitoring, A/B evaluation. - Understanding of RESTful APIs, microservices, and enterprise integration patterns. Bonus Points - Experience with the AWS AI/ML stack (Bedrock, AgentCore, Strands, SageMaker, Lambda, Step Functions). - Familiarity with responsible AI frameworks, bias testing tools, or AI governance practices. - Prior consulting or hyper-scaler experience where you’ve delivered for external clients under real constraints. - Contributions to open-source AI projects, AI research, or published technical writing.