Data Science Engineer, VP

1 week ago

Bengaluru, Karnataka, India Deutsche Bank Full time ₹ 20,00,000 - ₹ 25,00,000 per year

Role Description

We are seeking a seasoned Data Science Engineer to spearhead the development of intelligent, autonomous AI systems. The ideal candidate will have a robust background in agentic AI, LLMs, SLMs, vector DB, and knowledge graphs. This role involves designing and deploying AI solutions that leverage Retrieval-Augmented Generation (RAG), multi-agent frameworks, and 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

  • 13+ 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.

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