Responsibilities
- Architect, design, and implement scalable AI systems leveraging ChatGPT Enterprise, Glean AgenticAI, OpenAI APIs, and custom LLM pipelines
- Develop retrieval-augmented generation (RAG) architectures, vector database integrations, and multi-modal workflows
- Lead hands-on development, testing, and deployment of AI tools that enhance productivity, knowledge access, and decision-making
- Implement and maintain MLOps frameworks for LLM orchestration, model monitoring, prompt optimization, and performance tracking
- Partner with product, data, and operations teams to translate business challenges into AI-solutions
- Collaborate with IT, Legal, and Data Governance teams to ensure compliance, security, and responsible AI practices
- Mentor junior engineers and analysts, fostering a culture of technical excellence and ethical innovation
- Evaluate and integrate new generative AI tools to expand the organization’s enterprise AI capabilities
- Support AI policy and governance development to ensure systems meet enterprise standards for security, transparency, and accountability
Basic qualifications
- Education:
- Bachelor’s degree in Computer Science, Data Science, Information Systems, or related discipline (Master’s or MBA preferred)
- Experience:
- 5+ years of experience in AI/ML engineering, including 3+ years in a technical leadership role
- Proven expertise with ChatGPT Enterprise, Glean AgenticAI, OpenAI APIs, and large language model (LLM) technologies
- Strong programming experience in Python, LangChain, and orchestration frameworks for LLM development
- Hands-on experience deploying AI solutions on cloud platforms (Azure OpenAI, AWS Bedrock, GCP Vertex AI)
- Skills:
- Understanding of vector databases (Pinecone, FAISS, Weaviate) and retrieval-augmented generation architectures
- Deep understanding of data privacy, security, and AI governance best practices
- Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders
Preferred qualifications
- Experience deploying generative AI within regulated or global enterprise environments
- Familiarity with AI policy frameworks, governance models, and ISO/IEC 42001 standards
- Strong understanding of knowledge management, enterprise search, and retrieval-augmented generation (RAG) pipelines
- Experience with AI-powered workflow integration across enterprise tools such as ServiceNow, Salesforce, SharePoint, Microsoft 365, Zapier, Airtable, and Asana
- Demonstrated ability to lead geographically distributed technical teams and mentor engineers at multiple levels
- Exceptional communication and leadership skills with the ability to influence technical and non-technical stakeholders globally
Tags & focus areas
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