Job Description
Please note that visa sponsorship is not available at this time.
Key Responsibilities
• Design, build, and enhance production‑grade Generative AI applications for real‑world enterprise use cases.
• Develop GenAI applications using LangGraph, LangChain, or similar orchestration frameworks.
• Build and implement agentic workflows, Retrieval‑Augmented Generation (RAG), tool calling, prompt orchestration, and context management.
• Work with open‑weight models and hosted LLMs, including model integration and serving patterns.
• Integrate GenAI solutions with enterprise systems, APIs, backend services, data sources, and operational platforms.
• Design and implement reliable and scalable backend services supporting GenAI applications.
• Engineer solutions for production readiness, including logging, tracing, monitoring, evaluation, fallback mechanisms, error handling, and troubleshooting.
• Develop clean, maintainable, scalable, and testable code following strong software engineering practices.
• Evaluate existing technical designs, challenge weak approaches, and recommend practical and effective alternatives.
• Collaborate closely with application, data, platform, infrastructure, security, and business teams to deliver end‑to‑end GenAI solutions.
• Troubleshoot complex application, integration, model, and production issues and drive them through to resolution.
• Contribute to the continuous improvement of GenAI applications, architecture, engineering practices, and delivery processes.
• Work effectively in a fast‑moving environment with evolving requirements and incomplete information.
• Take ownership of delivery and demonstrate a pragmatic, hands‑on approach focused on building useful and reliable AI products rather than over‑engineering or hype.
Key Requirements
• 6+ years of software engineering experience, with recent hands‑on experience in Generative AI application development.
• Proven experience building and delivering production‑grade GenAI applications, beyond prototypes, experiments, or demos.
• Strong hands‑on experience with LangGraph, LangChain, or similar GenAI orchestration frameworks.
Practical experience with:
• RAG (Retrieval‑Augmented Generation)
• Agentic workflows
• Tool calling
• Prompt orchestration
• Context management
• Experience integrating LLMs into real‑world applications through APIs, backend services, and enterprise data sources.
• Experience working with open‑weight models, or strong hands‑on experimentation and understanding of open‑weight model deployment.
• Strong backend development skills using Python, Java, or similar programming languages.
• Good understanding of API design, distributed systems, scalability, and production resilience patterns.
• Experience with logging, tracing, observability, monitoring, evaluation, and troubleshooting for GenAI or backend applications.
• Familiarity with containerised deployment environments such as Kubernetes or OpenShift.
• Strong software engineering discipline with the ability to write clean, maintainable, scalable, and testable code.
• Strong analytical, debugging, and problem‑solving skills.
• Ability to assess technical designs critically and propose practical improvements.
• Strong communication and stakeholder management skills, with the ability to collaborate across business, application, data, infrastructure, platform, and security teams.
• High level of ownership, curiosity, enthusiasm, and willingness to get hands‑on with technical details.
• Comfortable working in a fast‑paced environment with evolving requirements and ambiguity.
• Pragmatic delivery mindset, with a focus on building reliable and useful solutions rather than over‑engineering.
Nice to Have:
• Experience with DeepAgent or similar agent frameworks.
• Experience with Langfuse, Elastic, or similar observability/search platforms.
• Experience using Redis for caching, conversation state, rate limiting, or queue‑backed workflows.
• Experience with vLLM or similar inference‑serving frameworks for open‑weight models.
• Experience deploying GenAI workloads across cloud or containerised environments.
• Experience in financial services or banking is not required.
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