Senior Applied AI Engineer

Responsibilities: Design, prototype, and deploy Generative AI solutions across customer-facing and internal business applications. Develop and optimize applications powered by large language models (LLMs), vector databases, prompt engineering techniques, and Retrieval-Augmented Generation (RAG) architectures. Build, deploy, and maintain AI-powered chatbots and virtual assistants, ensuring scalability, reliability, security, and compliance. Lead the development of AI agents for both digital and voice-based experiences, supporting real-time interactions for customers and employees. Evaluate, integrate, and implement third-party AI platforms, APIs, and emerging technologies to accelerate innovation and business value. Create proof-of-concept applications to assess new use cases such as document processing, classification, summarization, knowledge management, and workflow automation. Optimize prompts, model configurations, and orchestration frameworks to improve performance, accuracy, and cost efficiency. Partner closely with data, platform, and MLOps teams to ensure robust deployment processes, monitoring, observability, and governance practices. Collaborate with product managers, engineers, and business stakeholders to translate user needs into scalable AI solutions. Contribute to architectural discussions and establish best practices for enterprise AI adoption and integration.

Qualifications: Bachelor’s degree in Computer Science, Data Science, Statistics, Engineering, or a related technical field. Demonstrated success building and deploying production-grade AI chatbots, assistants, or conversational AI solutions within the last two years.6+ years of experience in AI/ML engineering, software engineering, data engineering, or natural language processing, including recent hands-on work with Generative AI and LLM-based applications.

Required Skills: Advanced Python programming skills and experience with modern AI frameworks and libraries such as Lang Chain, Hugging Face, OpenAI, Transformers, or similar technologies. Experience working with vector databases such as Pinecone, Weaviate, Chroma, FAISS, or equivalent platforms. Strong understanding of RAG architectures, prompt engineering, model evaluation, API orchestration, and LLM optimization techniques. Solid SQL skills and experience working with cloud-based data platforms and large-scale datasets. Ability to operate effectively in cross-functional teams and collaborate with product, engineering, analytics, and external partners. Strong communication skills with the ability to explain complex AI concepts and outcomes to both technical and non-technical audiences.

Preferred Qualifications: Experience working in highly regulated industries such as financial services, healthcare, insurance, or telecommunications. Hands-on experience developing voice AI, speech-enabled assistants, or conversational systems integrated with telephony platforms. Familiarity with modern data stack technologies, including cloud data warehouses, transformation tools, and data orchestration frameworks. Experience integrating AI solutions with CRM, customer service, or workflow management platforms. Exposure to document intelligence use cases including OCR, information extraction, summarization, semantic search, and personalized recommendations. Experience supporting customer-facing applications, contact center technologies, or enterprise-scale service operations.