Senior AI Engineer

We are seeking an experienced Senior AI Engineer to join our Engineering Team. The ideal candidate will have deep expertise in Large Language Models (LLMs), OCR, Retrieval-Augmented Generation (RAG), and Agentic AI systems, coupled with strong experience in cloud-native, enterprise-scale environments. This role is pivotal in driving our AI innovation strategy, leading end-to-end solution development, and delivering production-grade AI systems.

Responsibilities:

• Lead the full lifecycle of Generative AI solutions from design and development to deployment, encompassing LLM-powered workflows, RAG pipelines, OCR, and Agentic AI systems.
• Architect and implement secure, scalable AI infrastructures using GCP, AWS, or Azure.
• Apply LLMOps best practices, including fine-tuning, advanced prompt engineering, and model optimisation, to maximise performance and contextual accuracy.
• Design and maintain APIs and microservices (FastAPI, REST, Spring Boot) to integrate AI capabilities into enterprise systems.
• Build and optimise data pipelines and manage vector databases and Elasticsearch for efficient knowledge retrieval and decision-making.
• Implement MLOps, CI/CD, and DevOps pipelines using Kubernetes, Docker, Jenkins, and Ansible for automated deployment and monitoring.
• Ensure system reliability and observability through logging, monitoring, and infrastructure-as-code practices (e. g., ELK stack).
• Collaborate cross-functionally with product, engineering, and business teams to align AI solutions with organisational goals.
• Mentor and guide junior engineers, setting best practices for scalable, maintainable AI development.

Requirements:

• 4+ years in software engineering, with 2+ years focused on AI/ML in production.
• Proven experience shipping LLM-powered or ML-powered features at scale.
• Degree in Computer Science, Computer Engineering, or a related field.
• Background in fintech or document-heavy workflows is a strong differentiator.
• Deep expertise in LLMs and the full AI stack: prompt engineering, fine-tuning, RAG pipelines, agentic frameworks (LangChain, LlamaIndex, CrewAI, etc. )
• Proficiency in Python; strong engineering fundamentals (system design, APIs, observability).
• ML fundamentals: classification, regression, anomaly detection, NLP.
• Experience with vector databases (Pinecone, Weaviate, pgvector).
• Cloud-native (AWS/GCP/Azure) deploying and monitoring models in production.
• MLOps: experiment tracking (MLflow, W& B), CI/CD for models, drift monitoring.