Lead Software Engineer Python

8 - 12 years

0 Lacs

Posted:1 week ago| Platform: Shine logo

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On-site

Job Type

Full Time

Job Description

You will play a crucial role in the design and development of Python-based software components for AI-driven systems. Your responsibilities include: - Defining and maintaining coding standards, best practices, and architectural principles such as OOP, SOLID, and design patterns. - Collaborating with AI/ML engineers to integrate and productionize Machine Learning, Deep Learning, and Generative AI models. - Architecting and developing scalable RESTful APIs and backend systems using FastAPI or equivalent frameworks. - Advocating for performance optimization, testability, and non-functional requirements (NFRs) across all solutions. - Championing CI/CD practices, observability, and maintaining system reliability at scale. - Mentoring junior engineers to foster a culture of high-quality, maintainable software development. - Contributing to solution design for Retrieval-Augmented Generation (RAG) and Agentic AI workflows. Qualifications Required: - Bachelor's or Master's degree in Computer Science, Engineering, or a related field. - 8+ years of hands-on experience in backend software development, with significant expertise in Python. - Proven experience in leading and mentoring software development teams. - Strong command over software architecture, design patterns, and clean coding principles. - Experience in building and scaling API-based systems, preferably with FastAPI or similar frameworks. - Solid understanding of integrating ML/DL/GenAI models into production applications. - Familiarity with RAG, single and multi-agent architectures, and AI solution patterns. - Practical experience with AWS (Sagemaker, Bedrock) and/or Azure (ML Studio, OpenAIq Service). - Exposure to MLOps, model versioning, and observability tools. - Working knowledge of Java or Rust is a plus. - Experience designing software systems for cloud-native environments. - Prior experience working in the Power and Energy domain. - Familiarity with scalable data processing, real-time systems, or event-driven architectures. - Exposure to open-source tools and frameworks in the GenAI ecosystem (e.g., LangGraph, LlamaIndex, SmolAgents). - Deep commitment to quality, performance, and engineering excellence.,

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