We are looking forLead Software Engineer Python,
You'll make an impact by
- Lead the design and development of Python-based software components for AI-driven systems.
- Define and uphold coding standards, best practices, and architectural principles (OOP, SOLID, design patterns).
- Collaborate with AI/ML engineers to integrate and productionize Machine Learning, Deep Learning, and Generative AI models.
- Architect and develop scalable RESTful APIs and backend systems using FastAPI or equivalent frameworks.
- Advocate for performance optimization, testability, and non-functional requirements (NFRs) across all solutions.
- Champion CI/CD practices, observability (logging, metrics, tracing), and maintain system
- reliability at scale.
- Mentor junior engineers and create a culture of high-quality, maintainable software
- development.
- Contribute to solution design for RAG (Retrieval-Augmented Generation) and Agentic AI
- workflows.
- Collaborate with multi-disciplinary teams to align software solutions with AI and business goals.
Use your skills to move the world forward!
- 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.
