AI Platform / Data Engineering Lead
Location: New York, NY, USA
Duration: 12 Months + Extension
Bill Rate: $90/hr on C2C
Job Type: C2C/1099 Contract
Client: To Be Discussed Later
Work Authorization: US-Citizen, H-1B, OPT-EAD, GC-EAD
Job Description:
The ideal candidate will possess a strong background in Software Engineering, Data Engineering, Cloud Architecture, Platform Engineering, and Generative AI, with hands-on experience leveraging technologies such as GitHub Copilot, Claude Code CLI, Model Context Protocol (MCP), AI Agents, AWS Bedrock, OpenAI, Anthropic, and enterprise API integration frameworks to improve engineering productivity and accelerate software delivery.
This is a highly technical leadership role focused on modernizing the software development lifecycle through AI-powered engineering practices, cloud-native architecture, and scalable data platforms.
Key Responsibilities:
Design, develop, and implement enterprise-scale cloud-native data platforms and engineering solutions.
Build secure, scalable, highly available, and high-performance data architectures on AWS.
Develop robust ETL/ELT frameworks for ingesting, transforming, validating, and publishing enterprise data.
Design and optimize distributed data processing pipelines using Apache Spark and PySpark.
Lead enterprise adoption of AI-assisted software development tools including GitHub Copilot and Claude Code CLI.
8+ years of Software Engineering and Data Engineering experience.
3+ years of experience leading engineering teams or complex technical initiatives.
Design and implement AI-native engineering workflows using Model Context Protocol (MCP) and AI Agent architectures.
Build intelligent engineering solutions utilizing LLM integrations and AI orchestration frameworks.
Develop cloud-native microservices and enterprise APIs supporting modern AI-enabled applications.
Collaborate with enterprise architects, platform engineering teams, DevOps engineers, data engineers, and business stakeholders to deliver scalable technology solutions.
Implement CI/CD pipelines and Infrastructure as Code (IaC) using modern DevOps practices.
Define engineering standards, governance, and best practices for responsible AI adoption across enterprise platforms.
Drive modernization initiatives focused on developer productivity, platform engineering, automation, and cloud transformation.
Mentor engineering teams on AI-native software development methodologies and cloud-native architecture.
Ensure enterprise solutions meet security, compliance, scalability, availability, and performance requirements.
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