AI Development & Engineering
- Design, develop, and deploy AI-powered applications and intelligent automation solutions.
- Build customized AI agents capable of executing multi-step workflows, retrieving information, and interacting with enterprise systems.
- Integrate large language models (LLMs), including OpenAI, Anthropic Claude, and open-source models, into business applications.
- Develop Retrieval-Augmented Generation (RAG) solutions using vector databases and enterprise knowledge repositories.
- Implement AI solutions using Python, APIs, and modern AI frameworks such as LangChain, LangGraph, and LlamaIndex.
- Evaluate and optimize AI model performance, accuracy, reliability, and cost.
Automation & Systems Integration
- Identify opportunities to automate repetitive IT and business processes.
- Build AI agents that integrate with enterprise applications, databases, cloud platforms, and internal APIs.
- Develop automation workflows for IT support, infrastructure monitoring, reporting, and operational processes.
- Integrate AI solutions with Microsoft 365, ServiceNow, Salesforce, ERP systems, and other enterprise platforms.
- Partner with infrastructure and cybersecurity teams to ensure secure deployment and operation of AI solutions.
AI Infrastructure & Deployment
- Deploy AI applications within AWS, Microsoft Azure, or Google Cloud environments.
- Develop scalable APIs and microservices supporting AI applications.
- Implement CI/CD pipelines, containerization, and monitoring for AI workloads.
- Establish safeguards for data privacy, access control, model reliability, and responsible AI usage.
- Monitor AI application performance and troubleshoot production issues.
Business Collaboration
- Work directly with business leaders to identify AI use cases with measurable ROI.
- Translate business requirements into practical technical solutions.
- Develop proof-of-concept applications and transition successful solutions into production.
- Document AI architecture, workflows, integrations, and operational procedures.
- Provide technical guidance on AI adoption and emerging technologies.
- Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related field, or equivalent practical experience.
- 3+ years of software engineering experience, including hands-on AI/ML or generative AI development.
- Strong proficiency in Python and API development.
- Experience building applications using LLMs and generative AI technologies.
- Hands-on experience with AI frameworks such as LangChain, LangGraph, LlamaIndex, or similar tools.
- Experience developing AI agents, RAG applications, or workflow automation solutions.
- Familiarity with REST APIs, SQL databases, and cloud environments.
- Experience deploying applications in AWS, Azure, or Google Cloud.
- Strong understanding of software development lifecycle (SDLC), Git, and CI/CD practices.