AI Engineer

Job Title

Pay Range: $65hr - $70hr

• 3-5 years in Machine Learning, AI, or a related field, with production systems delivered.
• At least 1 year building custom Agentic AI applications.
• Strong Python skills and sound modern development practices.
• Hands-on experience with LLMs and prompt engineering across the full application lifecycle.
• Demonstrated experience building AI agents with Lang Graph.
• Familiarity with at least one enterprise cloud AI platform for building and deploying agentic applications, such as Azure AI Foundry, AWS Bedrock, or Google Gemini Enterprise, including cloud-native deployment practices.
• Working knowledge of REST APIs, Web Sockets, and event-driven systems.
• Proficiency with CI/CD tooling (Jenkins) and version control (Git).
• Fluency with AI-augmented development tools for rapid prototyping.
• Strong written and verbal communication skills.
• Analytical approach to problem-solving.
• Ability to work independently within a cross-functional team.
• Data layer curations and integration with source system for agentic application.

• Build AI agents and multi-agent systems using frameworks with Lang Graph and Lang Chain tools.
• Develop and tune prompt engineering workflows across multiple LLMs (GPT, Claude, LLaMA), balancing reliability, cost, and latency.
• Develop REST APIs, Web Socket services, and event-driven pipelines for real-time AI services that remain stable under load.
• Automate testing and releases through Jenkins CI/CD, and maintain code and documentation standards using Git, Jira, and Confluence.
• Deployment of AI Application in enterprise adhering to best practices.
• Use AI-augmented development tools such as Claude Code and Codex to accelerate delivery.
• Coordinate with platform, security, and product teams to deliver scalable, secure deployments.

• Familiarity with Databricks.
• Exposure to MLOps/LLMOps workflows and application monitoring.
• Knowledge of enterprise security, compliance, and governance for AI systems.
• Familiarity with code and model lifecycle management practices.