Data Science Engineer, AVP

Role Overview: You are a seasoned Data Science Engineer responsible for spearheading the development of intelligent, autonomous AI systems. Your role involves designing and deploying AI solutions that leverage Retrieval-Augmented Generation (RAG), multi-agent frameworks, and hybrid search techniques to enhance enterprise applications. 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. Qualifications Required: - 8+ 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. - Familiarity with RAG architectures and hybrid search methodologies. - Experience in developing AI solutions for specific industries such as healthcare, finance, or e-commerce. - Strong problem-solving abilities and analytical thinking. Excellent communication skills for cross-functional collaboration. Ability to work independently and manage multiple projects simultaneously.,