Software Engineer AI at Boehringer Ingelheim

Job description

Description In this role you will partner with product and engineering teams at Boehringer Ingelheim, in our BI X business to design, build, and integrate AI-powered solutions into applications that drive meaningful impact in healthcare. You will work across the full AI development lifecycle from rapidly prototyping LLM-based workflows to delivering reliable, well evaluated solutions that teams can build on. If you enjoy experimenting with emerging AI tools, working across disciplines, and turning ideas into working software, you will feel at home at BI X. Our global presence provides opportunity for all employees to collaborate internationally, offering visibility and opportunity to directly contribute to the companies´ success. We realize that our strength and competitive advantage lie with our people. We support our employees in a number of ways to foster a healthy working environment, meaningful work, mobility, networking and work-life balance. Our competitive compensation and benefit programs reflect Boehringer Ingelheim´s high regard for our employees. Locatiion is our Ridgefield, CT office. This is a collaborative team and we are 3 days on site. For these roles, we are unable to sponsor the successful candidate for work authorization in the U.S. The successful candidate must be authorized to work in the U.S. without employer sponsorship currently and in the future. Compensation Data This position offers a base salary typically between $75,000 and 181,000. The position may be eligible for a role specific discretionary bonus, relocation, and/or other compensation elements. We continuously review market data and may adjust salary ranges as needed in the future. Actual compensation will be based on job-related factors such as skills, experience, and qualifications, and other factors permitted by law. For an overview of our benefits please click here. Duties & Responsibilities • AI Integration & Development: Design and implement LLM-based features and pipelines using frameworks such as LangChain and LangGraph. Build and maintain Retrieval-Augmented Generation (RAG) systems, including vector store management, chunking strategies, embedding pipelines, and retrieval optimization. • Prototyping & Experimentation: Rapidly explore and evaluate new AI capabilities, tools, and approaches. Translate ambiguous problem statements into working prototypes that demonstrate value and inform product direction. • Collaboration & Requirements Gathering: Meet regularly with IT colleagues, product owners, and stakeholders to understand business needs and translate them into well-scoped AI engineering solutions. Communicate tradeoffs clearly across technical and non-technical audiences. • Application Support: Maintain and improve existing AI-integrated systems, troubleshoot pipeline failures, and take corrective action on issues related to retrieval quality, prompt behavior, or model outputs. • Project Participation: Contribute to project teams by delivering on assigned tasks and action items, participating in agile ceremonies, and supporting delivery against team goals. • Technical Awareness: Stay current with the rapidly evolving AI/ML landscape, including emerging LLM capabilities, new tooling, and best practices for responsible AI development. • Documentation: Produce clear documentation for AI workflows, integration patterns, prompt templates, and system architecture in support of both operational and project activities Requirements For these roles, we are unable to sponsor the successful candidate for work authorization in the U.S. The successful candidate must be authorized to work in the U.S. without employer sponsorship currently and in the future. Core Competencies • Proficiency in Python for AI/ML development and backend service integration. • Hands-on experience with LangChain and/or LangGraph for building agentic and multi-step LLM workflows. • Strong understanding of RAG pipelines end-to-end: document ingestion, chunking, embedding, vector storage, and query-time retrieval. • Familiarity with leading LLM providers and APIs (e.g., OpenAI, Anthropic, Azure OpenAI, AWS Bedrock). • Experience with evaluation and testing of AI systems, including retrieval quality metrics, LLM output assessment, and regression testing for prompt changes. Understanding of AI observability and experimentation tooling (e.g., LangSmith, Weights & Biases, or equivalent). • Working knowledge of vector databases and semantic search principles. • Solid foundation in software engineering practices: version control (Git), CI/CD, and containerization (Docker). • Effective communication skills with the ability to explain AI system behavior and limitations to non-technical stakeholders. Senior Software Engineer: • Associate degree in Computer Science or MIS with a minimum of 4 years experience; or Bachelor degree in Computer Science, or MIS, or related field with a minimum of 2 years of experience; or a Master degree in Computer Science, MIS, with minimum 1 year of experience; or relevant Business or IT experience of minimum of 4 years. • Hands-on experience building and shipping software in Python, with working knowledge of REST API development and integration patterns. • Demonstrated experience integrating LLMs into applications using frameworks such as LangChain, LangGraph, or equivalent. • Practical understanding of RAG architectures, including embedding models, vector databases (e.g., FAISS, Chroma, Pinecone, Weaviate), and retrieval strategies. • Familiarity with prompt engineering techniques, including few-shot prompting, chain-of-thought, and system prompt design. • Knowledge of software development lifecycle and experience working on agile project teams. Principal Software Engineer • Associate degree in Computer Science, MIS or a related field with a minimum of 7 years experience; or Bachelor degree in Computer Science, or MIS, with a minimum of 5 years of experience; or a Master degree in Computer Science, MIS, with a minimum 3 years of experience; or relevant Business or IT experience of minimum of 7 years. Minimum of 4 years of programming preferred. • Above noted requirements plus demonstrated ability to lead technical design decisions on AI systems,mentor junior engineers, and drive adoption of best practices across teams. Desired Skills, Experience and Abilities • Experience with agentic AI patterns including tool use, multi-agent orchestration, memory management, and reasoning loops using LangGraph or similar frameworks. • Exposure to healthcare data standards (e.g., FHIR/HL7) and regulated or privacy-sensitive environments. • Experience with structured output generation, function calling, and tool-augmented LLMs. • Comfort working with AI-assisted development environments (e.g., GitHub Copilot, Cursor) while maintaining code quality and security standards. • Comfort with lightweight discovery methods such as analytics, user interviews, and partnering with design and UX research to validate AI-driven solutions. • Interest in responsible AI practices, including bias evaluation, content moderation, and ethical deployment considerations. Eligibility Requirements : • Must be legally authorized to work in the United States without restriction. • Must be willing to take a drug test and post-offer physical (if required). • Must be 18 years of age or older.

Requirements

Must-haves

Locatiion is our Ridgefield, CT office. This is a collaborative team and we are 3 days on site.
Compensation Data
Duties & Responsibilities
AI Integration & Development: Design and implement LLM-based features and pipelines using frameworks such as LangChain and LangGraph. Build and maintain Retrieval-Augmented
Generation (RAG) systems, including vector store management, chunking strategies, embedding pipelines, and retrieval optimization.
Prototyping & Experimentation: Rapidly explore and evaluate new AI capabilities, tools, and approaches. Translate ambiguous problem statements into working prototypes that demonstrate value and inform product direction.
Application Support: Maintain and improve existing AI-integrated systems, troubleshoot pipeline failures, and take corrective action on issues related to retrieval quality, prompt behavior, or
model outputs.
Project Participation: Contribute to project teams by delivering on assigned tasks and action items, participating in agile ceremonies, and supporting delivery against team goals.
Technical Awareness: Stay current with the rapidly evolving AI/ML landscape, including emerging LLM capabilities, new tooling, and best practices for responsible AI development.
Documentation: Produce clear documentation for AI workflows, integration patterns, prompt templates, and system architecture in support of both operational and project activities
Core Competencies
Proficiency in Python for AI/ML development and backend service integration.
Hands-on experience with LangChain and/or LangGraph for building agentic and multi-step LLM workflows.
Strong understanding of RAG pipelines end-to-end: document ingestion, chunking, embedding, vector storage, and query-time retrieval.
Familiarity with leading LLM providers and APIs (e.g., OpenAI, Anthropic, Azure OpenAI, AWS Bedrock).
Experience with evaluation and testing of AI systems, including retrieval quality metrics, LLM output assessment, and regression testing for prompt changes.
Understanding of AI observability and experimentation tooling (e.g., LangSmith, Weights & Biases, or equivalent).
Working knowledge of vector databases and semantic search principles.
Solid foundation in software engineering practices: version control (Git), CI/CD, and containerization (Docker).
Effective communication skills with the ability to explain AI system behavior and limitations to non-technical stakeholders.
Hands-on experience building and shipping software in Python, with working knowledge of REST API development and integration patterns.
Demonstrated experience integrating LLMs into applications using frameworks such as LangChain, LangGraph, or equivalent.
Practical understanding of RAG architectures, including embedding models, vector databases (e.g., FAISS, Chroma, Pinecone, Weaviate), and retrieval strategies.

Nice-to-haves

None flagged.

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Platform: successfactors
Effort: medium