Job Summary
AI Agent Development & LLM Integration
• Build AI agents using frameworks like LangGraph, Autogen, Crew, or PydanticAI.
• Design and optimize prompt engineering workflows for LLMs (e.g., GPT, Claude, LLaMA).
• Develop modular, reusable components for agent orchestration and task automation.
Model Deployment & Infrastructure
• Deploy models using Databricks and Azure ML, integrating with enterprise-grade systems.
• Implement APIs, WebSockets, and event-driven architectures for real-time AI services.
• Collaborate with platform teams to ensure scalable and secure deployments.
Development & Automation
• Work with CI/CD pipelines using Jenkins to automate testing and deployment.
• Use GitHub Copilot, Windsurf, Codeium, or similar tools to accelerate development.
• Maintain high code quality and documentation standards using Git, Jira, and Confluence
Must-Have Skills:
• 8+ years of experience working Machine Learning / Artificial Intelligent or related fields.
• 8+ years of experience building Generative AI Applications for enterprise, deep understanding of workflows like RAG.
• Strong proficiency in Python and modern development practices.
• Hands-on experience with LLMs and prompt engineering.
• Experience building AI agents using at least one of Lang Graph, Crew, or PydanticAI.
• Familiarity with Databricks and cloud-native deployment strategies.
• Understanding of REST APIs, WebSockets, and event-driven systems.
• Familiarity with SDLC best practices and agile methodologies.
• Proficiency with using CI/CD tools (Jenkins) and version control (Git).
• Comfortable using AI-augmented development tools for rapid prototyping.
Nice-to-Have Skills:
• Exposure to MLOps/LLMOps workflows and model monitoring.
• Knowledge of enterprise security, compliance, and governance in AI systems.
• Exposure to best practices for code and model lifecycle management.
Soft Skills & Mindset:
• Strong analytical and problem-solving skills.
• Excellent communication and collaboration abilities.
• Self-starter with a growth mindset and curiosity for emerging AI trends.
• Ability to work in cross-functional teams and adapt to evolving priorities.
Key Responsibilities
AI Agent Development & LLM Integration
• Build AI agents using frameworks like LangGraph, Autogen, Crew, or PydanticAI.
• Design and optimize prompt engineering workflows for LLMs (e.g., GPT, Claude, LLaMA).
• Develop modular, reusable components for agent orchestration and task automation.
Model Deployment & Infrastructure
• Deploy models using Databricks and Azure ML, integrating with enterprise-grade systems.
• Implement APIs, WebSockets, and event-driven architectures for real-time AI services.
• Collaborate with platform teams to ensure scalable and secure deployments.
Development & Automation
• Work with CI/CD pipelines using Jenkins to automate testing and deployment.
• Use GitHub Copilot, Windsurf, Codeium, or similar tools to accelerate development.
• Maintain high code quality and documentation standards using Git, Jira, and Confluence
Must-Have Skills:
• 8+ years of experience working Machine Learning / Artificial Intelligent or related fields.
• 8+ years of experience building Generative AI Applications for enterprise, deep understanding of workflows like RAG.
• Strong proficiency in Python and modern development practices.
• Hands-on experience with LLMs and prompt engineering.
• Experience building AI agents using at least one of Lang Graph, Crew, or PydanticAI.
• Familiarity with Databricks and cloud-native deployment strategies.
• Understanding of REST APIs, WebSockets, and event-driven systems.
• Familiarity with SDLC best practices and agile methodologies.
• Proficiency with using CI/CD tools (Jenkins) and version control (Git).
• Comfortable using AI-augmented development tools for rapid prototyping.
Nice-to-Have Skills:
• Exposure to MLOps/LLMOps workflows and model monitoring.
• Knowledge of enterprise security, compliance, and governance in AI systems.
• Exposure to best practices for code and model lifecycle management.
Soft Skills & Mindset:
• Strong analytical and problem-solving skills.
• Excellent communication and collaboration abilities.
• Self-starter with a growth mindset and curiosity for emerging AI trends.
• Ability to work in cross-functional teams and adapt to evolving priorities.
Skill Requirements
AI Agent Development & LLM Integration
• Build AI agents using frameworks like LangGraph, Autogen, Crew, or PydanticAI.
• Design and optimize prompt engineering workflows for LLMs (e.g., GPT, Claude, LLaMA).
• Develop modular, reusable components for agent orchestration and task automation.
Model Deployment & Infrastructure
• Deploy models using Databricks and Azure ML, integrating with enterprise-grade systems.
• Implement APIs, WebSockets, and event-driven architectures for real-time AI services.
• Collaborate with platform teams to ensure scalable and secure deployments.
Development & Automation
• Work with CI/CD pipelines using Jenkins to automate testing and deployment.
• Use GitHub Copilot, Windsurf, Codeium, or similar tools to accelerate development.
• Maintain high code quality and documentation standards using Git, Jira, and Confluence
Must-Have Skills:
• 8+ years of experience working Machine Learning / Artificial Intelligent or related fields.
• 8+ years of experience building Generative AI Applications for enterprise, deep understanding of workflows like RAG.
• Strong proficiency in Python and modern development practices.
• Hands-on experience with LLMs and prompt engineering.
• Experience building AI agents using at least one of Lang Graph, Crew, or PydanticAI.
• Familiarity with Databricks and cloud-native deployment strategies.
• Understanding of REST APIs, WebSockets, and event-driven systems.
• Familiarity with SDLC best practices and agile methodologies.
• Proficiency with using CI/CD tools (Jenkins) and version control (Git).
• Comfortable using AI-augmented development tools for rapid prototyping.
Nice-to-Have Skills:
• Exposure to MLOps/LLMOps workflows and model monitoring.
• Knowledge of enterprise security, compliance, and governance in AI systems.
• Exposure to best practices for code and model lifecycle management.
Soft Skills & Mindset:
• Strong analytical and problem-solving skills.
• Excellent communication and collaboration abilities.
• Self-starter with a growth mindset and curiosity for emerging AI trends.
• Ability to work in cross-functional teams and adapt to evolving priorities.