Senior Technology Consultant - AI Native Engineer

Role: Senior Technology Consultant - AI Native Engineer

Location: New York, NY (Hybrid).

Type: Contract

Role Summary:

• We are seeking a hands-on Senior AI Native Engineer to design, build, and deliver production-grade AI agents, LLM-integrated applications, and developer productivity tooling on AWS.
• The ideal candidate is an experienced software engineer who remains actively involved in coding and uses GitHub Copilot and/or Claude Code CLI as part of their day-to-day development workflow. This role requires proven experience taking agentic AI or LLM-based solutions beyond proof-of-concept into production environments, with strong foundations in cloud-native engineering, Microservices, APIs, and DevOps.

Day to Day Job Duties:

• Design, develop, and deploy production-grade AI agents and agentic workflows.
• Build LLM-powered applications and integrate AI capabilities into enterprise systems and engineering workflows.
• Develop cloud-native services and AI solutions using AWS and AWS-native technologies.
• Build and maintain Microservices, REST APIs, event-driven services, and enterprise integrations.
• Use GitHub Copilot and/or Claude Code CLI as part of daily software development and engineering activities.
• Design and implement agent workflows using frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Strands, or Bedrock Agents.
• Build integrations using Model Context Protocol (MCP) and develop MCP servers where applicable.
• Develop internal developer platforms, engineering productivity tools, and reusable AI capabilities.
• Design and implement production-grade RAG solutions, including retrieval, relevance evaluation, and quality optimization.
• Integrate enterprise applications with AWS Bedrock, OpenAI, Anthropic, or other LLM APIs.
• Implement LLM observability, tracing, evaluation, and performance monitoring.
• Develop AI guardrails, audit logging, security controls, and prompt-injection defenses.
• Build automated testing and evaluation processes for AI/LLM applications.
• Implement CI/CD pipelines and support production deployment, monitoring, troubleshooting, and continuous improvement.
• Participate in architecture discussions and code reviews while remaining a strong hands-on individual contributor.

Basic Qualifications – Must Have:

• 7+ years of experience in hands-on software engineering, including Microservices, APIs, cloud-native applications, and enterprise software development.
• 3+ years of strong hands-on experience with AWS, DevOps/CI-CD, and cloud-native architecture.
• Proven hands-on experience personally building and deploying an AI agent, LLM integration, or agentic workflow into a production environment.
• Current hands-on development experience, with active individual-contributor coding experience within the last 6 months.
• Demonstrated day-to-day use of GitHub Copilot and/or Claude Code CLI for software development, with the ability to explain specific real-world use cases.

Technical Skills:

Agentic AI: AI Agents, Agentic Workflows, Tool Calling, Multi-Agent Systems

Agent Frameworks: LangGraph, LangChain, CrewAI, AutoGen, Strands, Bedrock Agents

Protocols: Model Context Protocol (MCP)

AI/LLM Platforms: AWS Bedrock, Anthropic APIs, OpenAI APIs, Azure AI Foundry

Cloud: AWS

Architecture: Microservices, REST APIs, Event-Driven Architecture, Cloud-Native Applications

AI Coding Tools: GitHub Copilot, Claude Code CLI

DevOps: Git, CI/CD, automated testing and deployment

LLM Observability: Langfuse, LangSmith, Braintrust, Weights & Biases

AI Patterns: RAG, Embeddings, Vector Search, LLM Evaluation, Guardrails

Strongly Preferred:

• Hands-on experience developing an MCP server or MCP-enabled enterprise integration.
• Production experience with LangGraph, LangChain, CrewAI, AutoGen, Strands, or Bedrock Agents.
• Strong production experience with AWS Bedrock.
• Experience building internal developer platforms or engineering productivity products.
• Experience implementing production RAG systems with measurable retrieval quality and evaluation frameworks.
• Experience implementing LLM observability and evaluation using Langfuse, LangSmith, Braintrust, or W&B.
• Experience with AI governance and Responsible AI controls, including guardrails, prompt-injection protection, access controls, and audit logging.
• Experience designing secure enterprise integrations between AI agents and internal systems.
• Strong software architecture, troubleshooting, communication, and problem-solving skills.