Data Science Engineer, AS

Role Description

We are seeking a Data Science Engineer to contribute to the development of intelligent, autonomous AI systems The ideal candidate will have a strong background in agentic AI, LLMs, SLMs, vector DB, and knowledge graphs. This role involves deploying AI solutions that leverage

• Retrieval-Augmented Generation (RAG)
• Multi-agent frameworks
• Hybrid search techniques to enhance enterprise applications.

Your key responsibilities

• Design & Develop Agentic AI Applications:Utilise frameworks like LangChain, CrewAI, and AutoGen to build autonomous agents capable of complex task execution.
• Implement RAG Pipelines: Integrate LLMs with vector databases (e.g., Milvus, FAISS) and knowledge graphs (e.g., Neo4j) to create dynamic, context-aware retrieval systems.
• Fine-Tune Language Models:Customise LLMs (e.g., Gemini, chatgpt, Llama) and SLMs (e.g., Spacy, NLTK) using domain-specific data to improve performance and relevance in specialised applications.
• NER Models: Train OCR and NLP leveraged models to parse domain-specific details from documents (e.g., DocAI, Azure AI DIS, AWS IDP)
• Develop Knowledge Graphs: Construct and manage knowledge graphs to represent and query complex relationships within data, enhancing AI interpretability and reasoning.
• Collaborate Cross-Functionally:Work with data engineers, ML researchers, and product teams to align AI solutions with business objectives and technical requirements.
• Optimise AI Workflows:Employ MLOps practices to ensure scalable, maintainable, and efficient AI model deployment and monitoring.

Your skills and experience

• 4+ years of professional experience in AI/ML development, with a focus on agentic AI systems.
• Proficient in Python, Python API frameworks, SQL and familiar with AI/ML frameworks such as TensorFlow or PyTorch.
• Experience in deploying AI models on cloud platforms (e.g., GCP, AWS).
• Experience with LLMs (e.g., GPT-4), SLMs (Spacy), and prompt engineering. Understanding of semantic technologies, ontologies, and RDF/SPARQL.
• Familiarity with MLOps tools and practices for continuous integration and deployment.
• Skilled in building and querying knowledge graphs using tools like Neo4j.
• Hands-on experience with vector databases and embedding techniques.
• Experience in developing AI solutions for specific industries such as healthcare, finance, or e-commerce.