Senior Agentic AI Engineer

Boeing

Senior Agentic AI Engineer developing AI-driven solutions for aerospace at Boeing India. Collaborating with stakeholders and leveraging machine learning in diverse projects.

Posted 7/3/2026full-timeBangalore • 🇮🇳 IndiaSeniorWebsite

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

Flexible work arrangements
Professional development opportunities

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

Machine LearningClassification ModelsRegression ModelsTokenizationEmbedding GenerationNamed Entity ExtractionContext SelectionRankingFilteringSummarization

Soft Skills

Strong CommunicationAnalytical SkillsProblem-Solving SkillsBusiness Development SkillsCollaboration