Artificial Intelligence Engineer

Key Responsibilities

Agentic Solution Development

• Design and implement enterprise AI solutions using agentic workflows and orchestration frameworks 
• Build multi-agent systems leveraging memory, planning, tool use, and MCP-style interactions 
• Develop production-ready AI workflows integrating enterprise systems, APIs, and business tools 
• Create reusable prompts, tools, agents, and orchestration pipelines 

Enterprise Integration & Automation

• Integrate AI agents with enterprise applications, APIs, databases, SharePoint, SAP, ServiceNow, Jira, etc. 
• Build secure tool-calling and data-access mechanisms for agent workflows 
• Implement AI-assisted automation use cases across engineering, operations, safety, and business domains 

Solution Architecture & Engineering

• Collaborate on architecture design for scalable AI systems 
• Develop backend services, APIs, orchestration layers, and deployment pipelines 
• Optimize AI workflows for latency, reliability, scalability, and observability 
• Support cloud-native deployments on Azure/AWS 

Value Realization

• Work closely with business stakeholders to identify impactful use cases 
• Quantify business impact including productivity, cost savings, quality improvements, and automation efficiency 
• Support pilot rollouts and transition of PoCs into production 

Collaboration & Innovation

• Contribute reusable frameworks, templates, and accelerators 
• Stay updated with evolving AI ecosystems including OpenAI, LangChain, AutoGen, CrewAI, MCP, and orchestration frameworks 
• Mentor junior developers and contribute to AI engineering best practices 

 

Required Skills

Technical Skills

• Strong programming experience in Python and/or Node.js 
• Hands-on experience with: 
• LangChain  
• LangGraph  
• CrewAI / AutoGen / Semantic Kernel 
• OpenAI APIs / Azure OpenAI 
• MCP concepts and integrations 
• Experience building REST APIs and enterprise integrations 
• Strong understanding of: 
• Agent orchestration 
• Tool calling 
• RAG architectures 
• Vector databases 
• Memory systems 
• Prompt engineering 
• Experience with cloud platforms (AWS preferred) 
• Familiarity with Docker, CI/CD, GitHub, and DevOps practices 

Good to have Skills

• Experience with enterprise AI governance and security 
• Knowledge of observability frameworks and evaluation methods 
• Experience deploying AI systems in enterprise environments 
• Exposure to frontend frameworks (React/Next.js) is a plus