Data Science Engineer, AS
- Bangalore, Karnataka
- Permanent
- Full-time
- Retrieval-Augmented Generation (RAG)
- Multi-agent frameworks
- Hybrid search techniques to enhance enterprise applications.
- Best in class leave policy
- Gender neutral parental leaves
- 100% reimbursement under childcare assistance benefit (gender neutral)
- Sponsorship for Industry relevant certifications and education
- Employee Assistance Program for you and your family members
- Comprehensive Hospitalization Insurance for you and your dependents
- Accident and Term life Insurance
- Complementary Health screening for 35 yrs. and above
- 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.
- 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.
- Training and development to help you excel in your career
- Coaching and support from experts in your team
- A culture of continuous learning to aid progression
- A range of flexible benefits that you can tailor to suit your needs