MLOps Technical Lead

Job Summary

This role is accountable for building, deploying, and maintaining robust machine learning pipelines and operational frameworks. The individual applies solid expertise in ML Ops and DevOps tools to automate workflows, optimize model lifecycle management, and ensure reliable delivery of ML solutions. They contribute to project success by implementing best practices, supporting process compliance, and providing technical input within the team.

Key Responsibilities

• Design and implement AI-driven front-end and back-end solutions using Python, React, and Azure, with seamless integration of AgenticAI, GenAI, LLMs, and Copilot-enabled developer experiences.
• Develop and operationalize Responsible AI protocols, ensuring fairness, transparency, and compliance in all AI-powered features.
• Architect and deploy cloud-native applications on Azure, utilizing scalable microservices, containerization, and modern DevOps pipelines.
• Engineer modular front-end frameworks and responsive user interfaces that facilitate natural language interactions, leveraging LLM APIs and custom GenAI models.
• Integrate advanced analytics and business intelligence tools powered by AI, delivering actionable insights to corporate stakeholders.
• Implement robust API gateways and orchestration layers between AI models, data sources, and user-facing applications.
  Continuously monitor, test, and refine AI solutions for performance, security, and ethical compliance, adhering to the latest Responsible AI guidelines.
• Collaborate with cross-functional teams to prioritize and deliver features from the product backlog, with a focus on rapid prototyping and iterative improvement.

Skill Requirements

• Proven expertise (5+ years) in full stack AI engineering, including design, development, and deployment of AI-powered applications.
• Strong proficiency in Python for AI/ML model development, orchestration, and integration.
• Advanced React skills for building dynamic, GenAI-enabled user interfaces.
• Extensive experience with Azure cloud services (e.g., Storage, Azure OpenAI, Azure DevOps, Service bus, Azure Functions, etc).
• Demonstrated knowledge in implementing Responsible AI practices (explainability, bias mitigation, governance, ethical AI).
• Hands-on experience with LLMs (e.g., GPT-based models), GenAI frameworks, or Copilot tools for AI-augmented development workflows.
• Familiarity in cloud-native development, and CI/CD automation.
  Strong background in modern application architecture, API design, and secure integration of AI services.
• Familiarity with agile methodologies, version control (Git/Azure DevOps), and collaborative project delivery in cross-functional teams.
• Knowledge in Angular, React, .Net, Azure Full stack, RESTful microservices and Pega

Other Requirements

• Knowledge in Angular, React, .Net, Azure Full stack, RESTful microservices and Pega