Lead Software Engineer (AI/ML &Generative AI)

Lead Software Engineer (AI/ML &Generative AI)

Key Responsibilities

• Mentorship:Lead, mentor, and empower a team of junior and mid-level engineers, fosteringa culture of technical excellence and collaborative problem-solving. Guide the project from conception to completion, ensuring alignment withbusiness goals and on-time delivery. Champion and enforce software engineering best practices, including codereviews, design patterns, and efficient resource utilization.
• Design & Development:Own the end-to-end design, development, and deployment of Generative AIsolutions using agentic frameworks such as LangGraph, AutoGen, orSemantic Kernel. Build robust, scalable, and secure backend services and REST APIs usingPython and FastAPI. Design and implement resilient database schemas and ensure seamlessintegration with our cloud infrastructure.
• DevOps & Deployment:Own CI/CD pipeline for Python-based applications using tools like GitHubActions or Jenkins. Ensure smooth build, testing, and deployment processes, with a focus onautomation and reliability.

Core Qualifications

• 7+ years of professional software engineering experience, with a strong foundationin design principles, architectural patterns, and cloud computing.
• 1+ years of demonstrated, hands-on experience building or integrating GenerativeAI solutions (e.g., RAG pipelines, function calling, agentic workflows).
• Proficiency in Python and its ecosystem of data science and web developmentpackages.
• Proven experience building and maintaining CI/CD pipelines (GitHub Actions,Jenkins).
• Deep understanding of REST APIs, API security best practices, and Object-Oriented Programming (OOP) concepts.
• Solid experience with at least one major cloud provider (Azure, AWS, or GCP).

Nice-to-Have

• Experience as a full-stack engineer, with the ability to contribute to front-end (UI)development.
• Strong knowledge of MLOps principles, including monitoring ML pipelines anddefining robust evaluation metrics.
• Hands-on experience with Databricks and MLflow for managing machine learningexperiments and workflows.

Tech Stack

• Primary Language: Python
• Cloud Platforms: Azure / AWS / GCP
• CI/CD & DevOps: GitHub Actions / Jenkins , Docker
• AI/ML Frameworks: LangGraph, AutoGen, Semantic Kernel, (Nice to have)Scikit-learn, (Nice to have)TensorFlow/PyTorch
• API Frameworks: FastAPI / Flask
• Databases: Relational (e.g., SQL Server)