Senior Agentic AI Engineer
Boeing
Bengaluru, Karnataka, IndiaSENIOR
Hybrid
Job Description
Develop solutions in agentic AI and RAG workflows.
Responsibilities
- Collaborate with business stakeholders to understand project requirements and objectives and to translate vague business needs into clear data science and retrieval problem statements.
- Decompose complex problems into manageable tasks and develop end-to-end data-driven solutions architect that include modeling, retrieval, and integration into workflows, leading the way from capabilities to solutioning
- Should demonstrate strong communication and business development skills, lead Business transformation strategies, workshops with business stakeholders in defining strategic needs, develop AI/Agentic AI roadmap to deliver on key business KPIs
- Determine and develop the most appropriate machine learning and retrieval models (classification, regression, unsupervised methods, and retrieval pipelines) to resolve business problems
- Design and optimize retrieval-augmented generation (RAG) patterns that support agent workflows and decision-making.
- Define retrieval strategies for enterprise content sources, structured data, unstructured documents, and operational systems.
- Partner with platform engineers and data owners to enable secure access to approved knowledge sources.
- Develop methods for context selection, ranking, filtering, summarization, and grounding to improve response quality.
- Evaluate retrieval performance, relevance, latency, and answer fidelity across use cases.
- Create testing and benchmarking approaches for retrieval quality and downstream agent outcomes.
- Help define guardrails for data access, permissions, privacy, and information sensitivity.
- Work with governance and security stakeholders to ensure enterprise data is used in a compliant and auditable way.
- Contribute to reusable patterns, standards, and documentation for RAG-enabled agent capabilities.
- Support integration of retrieval systems with orchestration frameworks, APIs, and AI platform components.
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.
- Proficiency working with configuration and data formats such as Python, JSON, and YAML.
- Familiarity with enterprise search platforms, vector databases (e.g., Pinecone, Milvus, Weaviate), and knowledge retrieval pipelines.
- Knowledge of hybrid retrieval methods combining keyword and semantic search.
- Familiarity with retrieval benchmarking, relevance scoring, and human evaluation workflows.
- Understanding of prompt design patterns that improve grounding and citation quality.
- Typically, 12-16 years’ related work experience or relevant military experience. Advanced degree (e.g. Bachelor, Master, etc.) preferred but not required.
Benefits
- Competitive salary
- Work-life balance