Vanguard is looking to solve business problems by designing scalable, secure, and high-quality AI capabilities that power critical business use cases using Generative AI and Retrieval-Augmented Generation (RAG)
Requirements
- Hands-on experience building GenAI applications and RAG systems end-to-end
- Strong proficiency in Python for ML/LLM development
- Experience with vector databases (e.g., pgvector, Pinecone, Weaviate, FAISS) and embedding models
- Knowledge of LLM frameworks (LangChain, LlamaIndex, Transformers, etc.)
- Strong understanding of cloud environments (AWS/Azure) and containerized deployments
- Solid software engineering foundations APIs, microservices, version control, testing, CI/CD
- Experience with data pipelines, ETL/ELT, and processing unstructured data
Responsibilities
- Architect, build, and deploy RAG pipelines, including chunking, embeddings, vector stores, retrieval, ranking, grounding, and evaluation.
- Design and implement Graph RAG solutions leveraging knowledge graphs for multi-hop reasoning and structured retrieval.
- Build robust, scalable ML/LLM services using Python (and Java where applicable) with well-designed APIs and microservices.
- Develop data processing pipelines for ingestion, transformation, metadata extraction, and indexing.
- Implement observability, monitoring, evaluation harnesses, automated testing, and CI/CD for GenAI services.
- Optimize retrieval quality, response accuracy, latency, and cost across model + retrieval layers.
- Apply responsible AI, security, and governance practices for LLM systems (e.g., content filtering, guardrails, model monitoring)
Other
- 3+ years of experience as an ML Engineer, AI Engineer, or similar role
- Excellent communication skills and ability to work in cross-functional teams
- Ability to work in a hybrid working model with enhanced flexibility
- Vanguard is not offering visa sponsorship for this position
- A builder who loves engineering elegant, reliable systems that scale someone who understands both machine learning and strong software engineering practices
