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Description:
We are looking for an Enterprise AI Engineer to design, build, and deploy AI-powered internal automation solutions and employee copilots across functions such as IT, HR, Finance, Procurement, Operations, and Customer Support. This role will focus on applying LLMs, workflow automation, enterprise search, and agentic AI to improve productivity, simplify processes, and enable intelligent self-service across the organization.
The ideal candidate has hands-on experience building GenAI applications, internal copilots, RAG pipelines, API integrations, and workflow automations in enterprise environments, with strong attention to security, governance, and scale.
Key Responsibilities
Description:
We are looking for an Enterprise AI Engineer to design, build, and deploy AI-powered internal automation solutions and employee copilots across functions such as IT, HR, Finance, Procurement, Operations, and Customer Support. This role will focus on applying LLMs, workflow automation, enterprise search, and agentic AI to improve productivity, simplify processes, and enable intelligent self-service across the organization.
The ideal candidate has hands-on experience building GenAI applications, internal copilots, RAG pipelines, API integrations, and workflow automations in enterprise environments, with strong attention to security, governance, and scale.
Key Responsibilities
- Design and develop AI copilots, virtual assistants, and internal automation workflows for enterprise use cases.
- Build solutions using LLMs, prompt engineering, RAG, agent orchestration, and enterprise search.
- Integrate AI solutions with enterprise platforms such as ServiceNow, Workday, Microsoft 365, SharePoint, Slack, Teams, HRIS, CRM, ERP, and ticketing systems.
- Develop and maintain backend services, APIs, orchestration layers, and automation pipelines using Python and related frameworks.
- Partner with business stakeholders across IT, HR, Finance, Procurement, and Operations to identify high-value use cases and convert them into production solutions.
- Implement document understanding, knowledge retrieval, and task automation across structured and unstructured enterprise data.
- Establish best practices for prompt management, model evaluation, guardrails, observability, and LLMOps.
- Ensure enterprise-grade standards for security, privacy, access control, compliance, and governance.
- Monitor solution performance, user adoption, and business impact; iterate based on feedback and usage patterns.
- Support production deployments, troubleshooting, and continuous enhancement of AI systems.
- Bachelor s or Master s degree in Computer Science, Engineering, Data Science, Information Systems, or related field.
- 5 years of software engineering, automation, or AI/ML engineering experience.
- 2 years of hands-on experience building GenAI/LLM applications in enterprise settings.
- Strong programming skills in Python and experience with REST APIs, microservices, and backend integration.
- Experience with LLM frameworks such as LangChain, LangGraph, Semantic Kernel, LlamaIndex, or similar.
- Experience building RAG pipelines using vector databases and enterprise knowledge sources.
- Hands-on experience with one or more cloud AI platforms such as Azure OpenAI, AWS Bedrock, Google Vertex AI, or equivalent.
- Experience integrating with enterprise tools such as ServiceNow, Workday, Microsoft 365, SharePoint, Teams, Slack, Jira, Salesforce, or similar systems.
- Solid understanding of authentication, authorization, role-based access, data privacy, and enterprise security controls.
- Strong communication skills with the ability to work cross-functionally with technical and business teams.
- Experience with agentic workflows / multi-step task automation.
- Familiarity with MCP, tool calling, function calling, and agent orchestration patterns.
- Experience with Moveworks, Glean, Microsoft Copilot Studio / Power Virtual Agents, or similar enterprise AI platforms.
- Experience with workflow automation tools such as Power Automate, UiPath, ServiceNow Flow Designer, or equivalent.
- Exposure to LLMOps, model monitoring, prompt/version management, evaluation frameworks, and AI governance.
- Experience in large enterprise environments with shared services, ITSM, HR, procurement, or employee support use cases.