Senior AI Engineer

We are looking for a Senior AI Engineer with deep specialization in Retrieval-Augmented Generation (RAG) and embedding systems. The ideal candidate has a proven track record of building end-to-end AI solutions from ideation through to production deployment and thrives at the intersection of language models, vector search, and applied NLP. This is a hands-on, high-ownership role. You will design and ship RAG pipelines, own embedding infrastructure, integrate AI capabilities into chatbot products, and work with diverse data sources to build systems that actually work in production.
Responsibilities:
RAG pipeline ownership: Ideate, architect, build, and deploy end-to-end RAG systems from scratch through to production.
Embedding systems: Select, evaluate, and fine-tune embedding models; manage vector stores and optimize retrieval quality.
Advanced chunking: Implement late chunking and other segmentation strategies to maximize context fidelity and retrieval precision.
Multi-source data integration: Connect and ingest from diverse sources, including SQL/NoSQL databases, PDFs, web content, Confluence, SharePoint, and real-time APIs.
Chatbot integration: Embed RAG and LLM components into conversational AI products using LangChain, LlamaIndex, or custom orchestration layers.
Evaluation and quality: Own retrieval evaluation frameworks (RAGAS, triad evals) and iterate on pipelines based on precision, recall, and relevance metrics.
Deployment and observability: Deploy and monitor LLM services on cloud infrastructure with robust logging, alerting, and MLOps practices.
Collaboration: Partner with product and engineering teams to deliver low-latency, reliable AI experiences at scale.
Requirements:
5+ years of total AI/ML engineering experience, with 3+ years in LLM engineering.
Hands-on experience designing and deploying RAG systems end-to-end.
Deep familiarity with embedding models (OpenAI Ada, Cohere, BGE, E5) and vector databases (Pinecone, Weaviate, Chroma, pgvector).
Strong command of Python and LLM orchestration frameworks (LangChain, LlamaIndex, Haystack).
Experience working with multiple data source types: structured, unstructured, and real-time.
Practical knowledge of late chunking and other advanced retrieval strategies.
Familiarity with cloud deployment (AWS / GCP / Azure) and containerization (Docker, Kubernetes).
Strong problem-solving instincts and a bias for building things that work in production.
Good to Have:
Experience with agentic frameworks (AutoGen, CrewAI, or custom agents).
Exposure to graph-based RAG or knowledge graph integration.
Open-source contributions in the AI/ML space.

Experience
5-8 yrs