Senior Agentic AI Engineer

Required Qualifications

• Demonstrated experience working with stakeholders to create data science problem statements from vague business requirements.
• Strong understanding and hands-on experience with natural language processing (NLP) techniques (tokenization, embedding generation, summarization, named entity extraction, etc.).
• Familiarity with large language models and their applications in retrieval-augmented generation and agent workflows.
• Practical experience developing machine learning regression and multi-class classification models, especially under imbalanced data conditions.
• Demonstrated experience with RAG architectures and retrieval systems in production or enterprise environments.
• Strong understanding of how LLMs use retrieved context within agent workflows.
• Familiarity with retrieval concepts such as chunking, embeddings, vector search, metadata filtering, hybrid retrieval, reranking, and query rewriting.
• Ability to evaluate retrieval quality and answer grounding for accuracy and relevance.
• Strong analytical and problem-solving skills.
• Excellent written and verbal communication skills.
• Ability to work effectively across development, data, product, and governance teams.
• Experience with data access controls, privacy, and security considerations.
• Proficiency working with configuration and data formats such as Python, JSON, and YAML.
• Experience enabling agentic RAG or retrieval-driven workflow automation.
• Familiarity with enterprise search platforms, vector databases (e.g., Pinecone, Milvus, Weaviate), and knowledge retrieval pipelines.
• Experience with orchestration frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar tools.
• Knowledge of hybrid retrieval methods combining keyword and semantic search.
• Experience with observability, tracing, and evaluation for retrieval and generation pipelines.
• Familiarity with retrieval benchmarking, relevance scoring, and human evaluation workflows.
• Experience with document parsing, knowledge ingestion, and content normalization.
• Understanding of prompt design patterns that improve grounding and citation quality.
• Experience with security, compliance, and data governance controls in enterprise AI systems.
• Experience working in regulated or large-scale enterprise environments.