AI Engineer

Role: AI Engineer 
Location: Remote 
Duration: 6+ Months 

Enterprise Applications, Generative AI & Agentic AI
Role Summary
We are seeking a hands-on AI Engineer to design, prototype, build, and deploy AI-powered enterprise solutions that improve business outcomes and engineering productivity. The role spans the full lifecycle from solution design and architecture through implementation, deployment, monitoring, and operational support, with Generative AI capabilities embedded where they deliver measurable value.
Required Qualifications
• Demonstrated enterprise application engineering experience, including architecture, integration, testing, deployment, and production support.
• Hands-on experience delivering AI-enabled solutions using LLMs, RAG, intelligent agents, or comparable Generative AI patterns.
• Ability to design secure, scalable solutions with appropriate evaluation, observability, governance, and Responsible AI controls.
• Strong communication and collaboration skills across business, architecture, engineering, data, security, and platform stakeholders.
Preferred Technology Experience
Area Preferred Experience
Programming Python, Java, C#, JavaScript, and TypeScript
Cloud & AI Platforms Google Cloud Platform, Vertex AI, Gemini models, Azure AI, Azure OpenAI, Anthropic Claude APIs, and OpenAI APIs; GECP experience where applicable
Agentic AI LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen, or comparable agent frameworks
Generative AI LLM endpoints, prompt engineering, embeddings, vector databases, RAG, evaluation, guardrails, and model optimization
Enterprise Engineering REST APIs, microservices, event-driven architectures, enterprise integration patterns, and secure application design
Developer Productivity Claude Code, GitHub Copilot, Microsoft Copilot, Gemini Code Assist, GitHub, and Azure DevOps
Data & Platforms Databricks, SQL, data engineering, analytics platforms, and enterprise knowledge sources
DevOps & Operations CI/CD, Docker, Kubernetes, Infrastructure as Code, monitoring, logging, and production support

Ideal Candidate
• Combines hands-on technical depth with sound architectural judgment.
• Moves effectively from rapid experimentation to reliable enterprise delivery.
• Communicates clearly and builds alignment across technical and business teams.

Key Outcomes & Responsibilities
Solution Delivery
• Design, develop, and deploy Generative AI and Agentic AI solutions for enterprise use cases.
• Rapidly validate ideas, then mature successful proofs of concept into secure, scalable, production-ready solutions.
AI Engineering & Integration
• Integrate LLM and AI endpoints, enterprise data sources, APIs, and business systems.
• Build Retrieval-Augmented Generation patterns, intelligent agents, and orchestrated workflows with appropriate human oversight.
• Use coding assistants such as Claude Code, GitHub Copilot, Microsoft Copilot, and Gemini Code Assist to accelerate delivery and improve developer productivity.
Collaboration, Governance & Operations
• Collaborate across business, architecture, engineering, data, security, and platform teams to prioritize and deliver high-value AI use cases.
• Apply security, observability, governance, evaluation, and Responsible AI practices throughout the solution lifecycle.