AI Engineer

MLOps, Python/Ruby, LLM APIs, foundation models, embeddings, vector databases, RAG workflows, and agentic AI systems including MCP, SQL, distributed data platforms (eg, Spark), and cloud-native infrastructure (eg, AWS, GCP, or on-prem hybrid)

Proven years of experience in MLOps, data engineering, or software development, with a recent focus on GenAI, LLMs, and advanced analytics. Understanding of software engineering practices such as version control, CI/CD containerisation, and monitoring, particularly within ML or MLOps context. Proficiency in Python and/or Ruby, with experience in ML libraries (scikit-learn, TensorFlow, PyTorch) and frameworks such as FastAPI, LangChain, or similar. Hands-on experience with LLM APIs, foundation models, embeddings, vector databases, RAG workflows, and agentic AI systems including MCP. Experience with large-scale datasets using SQL, distributed data platforms (eg, Spark), and cloud-native infrastructure (eg, AWS, GCP, or on-prem hybrid). Strong data visualisation and communication skills, with the ability to explain complex models to both technical and non-technical audiences. Ability to translate ambiguous business problems into structured AI/ML solutions, and to manage multiple projects independently in a fast-paced environment. Prior experience collaborating with cross-functional teams including data engineers, developers, and UI/UX designers. B.S. Degree in Computer Science, Engineering, Statistics, Data Science, or equivalent work experience.

Preferred Qualifications

Past experience in generating sales insights and analytics using AI. Strong experience translating business questions into AI/ML solutions and communicating results to senior leaders and diverse audiences. Experience in revenue forecasting, or commercial operations. Proven experience with GenAI frameworks (LangChain, LlamaIndex, etc.), anomaly detection, and causal inference models. Familiarity with distributed systems technologies such as RabbitMQ, Redis, and Valkey, and with vector knowledge graph data modelling. Advanced Degree (MS or Ph.D.) in Computer Science, Electrical Engineering, Statistics, Data Science, or a similar quantitative field.