Senior Product Software Engineer | RAG | AI

Lensa is a career site that helps job seekers find great jobs in the US. We are not a staffing firm or agency. Lensa does not hire directly for these jobs, but promotes jobs on LinkedIn on behalf of its direct clients, recruitment ad agencies, and marketing partners. Lensa partners with DirectEmployers to promote this job for Wolters Kluwer. Clicking "Apply Now" or "Read more" on Lensa redirects you to the job board/employer site. Any information collected there is subject to their terms and privacy notice.

Job Description

We are seeking an experienced Senior Generative AI Software Engineer with full-stack expertise in .NET and Python to join our development team. In this role, you will architect, develop, and implement cutting-edge AI-powered solutions that transform how our customers interact with our products. You will be responsible for building production-scale generative AI systems, including RAG architectures, LLM orchestration, and agentic AI applications, while maintaining our high standards for scalable, secure cloud-based solutions. Working in an Agile environment, you'll lead technical innovation while mentoring team members and collaborating with cross-functional partners to deliver AI solutions that create measurable business impact.

Key Responsibilities

• Design and develop enterprise-scale generative AI applications using .NET Core, C#, Python, and React, ensuring they are secure, performant, and cost-optimized
• Architect and implement sophisticated RAG (Retrieval-Augmented Generation) systems using vector databases and hybrid search strategies
• Build and integrate LLM-powered features using frameworks like LangChain, Semantic Kernel, and custom orchestration solutions
• Create responsive AI-driven user interfaces using React, TypeScript, and modern front-end practices that effectively communicate AI capabilities and limitations
• Develop robust APIs and microservices that expose AI capabilities while implementing proper governance, monitoring, and safety measures
• Lead the evaluation and selection of AI models, balancing performance, cost, and business requirements
• Implement containerized AI applications using Docker and Kubernetes with proper resource management for GPU workloads
• Establish AI governance practices including bias detection, content safety, prompt injection prevention, and compliance requirements
• Mentor team members in both traditional software engineering and emerging AI practices
• Collaborate with product managers and stakeholders to translate business needs into innovative AI solutions
• Monitor AI system performance, implement cost optimization strategies, and ensure reliable production operations
• Stay current with the rapidly evolving GenAI landscape through research, experimentation, and community engagement

Key Requirements

• 5+ years of experience in full-stack development with expertise in .NET/C# and strong proficiency in Python
• 2+ years hands-on experience building production applications with LLMs (GPT-4, Claude, Gemini, or open-source models)
• Proven experience implementing RAG systems with vector databases (Pinecone, Weaviate, Azure AI Search)
• Strong proficiency in AI/ML frameworks including Hugging Face Transformers, LangChain, LlamaIndex, or similar
• Experience with prompt engineering, model evaluation, and LLM optimization techniques

Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.

Compensation

Target salary range CA, CT, CO, DC, HI, IL, MD, MN, NY, RI, WA: $114,750 - $160,450

EQUAL EMPLOYMENT OPPORTUNITY Wolters Kluwer U. S. Corporation and all of its subsidiaries, divisions and customer/business units is an Equal Opportunity / Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.

If you have questions about this posting, please contact [email protected]

Qualifications

• 5+ years of experience in full-stack development with expertise in .NET/C# and strong proficiency in Python
• 2+ years hands-on experience building production applications with LLMs (GPT-4, Claude, Gemini, or open-source models)
• Proven experience implementing RAG systems with vector databases (Pinecone, Weaviate, Azure AI Search)
• Strong proficiency in AI/ML frameworks including Hugging Face Transformers, LangChain, LlamaIndex, or similar
• Experience with prompt engineering, model evaluation, and LLM optimization techniques

Benefits

• Target salary range CA, CT, CO, DC, HI, IL, MD, MN, NY, RI, WA: $114,750 - $160,450

Responsibilities

• In this role, you will architect, develop, and implement cutting-edge AI-powered solutions that transform how our customers interact with our products
• You will be responsible for building production-scale generative AI systems, including RAG architectures, LLM orchestration, and agentic AI applications, while maintaining our high standards for scalable, secure cloud-based solutions
• Working in an Agile environment, you'll lead technical innovation while mentoring team members and collaborating with cross-functional partners to deliver AI solutions that create measurable business impact
• Design and develop enterprise-scale generative AI applications using .NET Core, C#, Python, and React, ensuring they are secure, performant, and cost-optimized
• Architect and implement sophisticated RAG (Retrieval-Augmented Generation) systems using vector databases and hybrid search strategies
• Build and integrate LLM-powered features using frameworks like LangChain, Semantic Kernel, and custom orchestration solutions
• Create responsive AI-driven user interfaces using React, TypeScript, and modern front-end practices that effectively communicate AI capabilities and limitations
• Develop robust APIs and microservices that expose AI capabilities while implementing proper governance, monitoring, and safety measures
• Lead the evaluation and selection of AI models, balancing performance, cost, and business requirements
• Implement containerized AI applications using Docker and Kubernetes with proper resource management for GPU workloads
• Establish AI governance practices including bias detection, content safety, prompt injection prevention, and compliance requirements
• Mentor team members in both traditional software engineering and emerging AI practices
• Collaborate with product managers and stakeholders to translate business needs into innovative AI solutions
• Monitor AI system performance, implement cost optimization strategies, and ensure reliable production operations
• Stay current with the rapidly evolving GenAI landscape through research, experimentation, and community engagement