Ai Engineer Jobs

About The Position

Requirements

• 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.
• 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.

Nice To Haves

• 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.

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.
• Lead AI technical delivery within cross-functional squads alongside designers, strategists, and client stakeholders.
• Implement responsible AI practices including red-teaming, bias testing, evaluation harnesses, and human-in-the-loop design.
• 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.
• Share knowledge across the engineering team through documentation, tech talks, and hands-on mentoring.

Benefits

• Unlimited access to Go1’s learning library
• Support from our internal performance coach
• Hybrid working (1-2 days per week in the office)
• Monthly anchor days
• Team lunches
• Annual offsite