Principal Software Engineer
Diebold Nixdorf6 months ago
Mumbai, IndiaStaff+
Responsibilities
- Architect and implement enterprise-grade AI, generative AI, and agentic AI systems.
- Develop, test, document, deploy, optimize, and support complex AI-driven products and features.
- Design AI systems involving machine learning, deep learning, NLP, computer vision, predictive analytics, RAG, vector search, and multi-agent workflows.
- Lead MLOps and deployment activities, including model monitoring, observability, and lifecycle management across cloud and on-premises environments.
- Define AI architecture, coding standards, engineering practices, and responsible AI guardrails.
- Review requirements, designs, code, use cases, and unit tests, and lead technical workshops and architectural alignment.
- Collaborate with customers, product teams, business stakeholders, QA leads, and engineering teams to deliver AI solutions.
- Mentor and motivate engineering teams and help assess engineering skills and capabilities.
- Evaluate emerging AI technologies and lead adoption of frameworks, tools, and technologies.
- Represent the product team externally and manage priorities across a global, cross-functional environment.
Requirements
- 13+ years of software engineering experience, including at least 7 years of AI/ML development experience with generative AI and agentic AI systems.
- Full-time bachelor's and/or master's degree in engineering with a minimum 60% grade.
- Strong Python programming proficiency and experience building enterprise-grade AI/ML and LLM applications.
- Deep experience with TensorFlow, PyTorch, scikit-learn, Keras, Hugging Face, LangChain, LangGraph, or AutoGen.
- Proficiency in machine learning, deep learning, NLP, generative AI, RAG architecture, vector search, and multi-agent workflows.
- Experience developing, deploying, optimizing, and scaling AI/ML, LLM, and agentic AI models across cloud and on-premises environments.
- Expertise in data engineering, feature engineering, embedding pipelines, and large-scale data processing.
- Hands-on experience with Docker, Kubernetes, CI/CD, MLflow, and AWS, GCP, or Azure.
- Demonstrated experience leading customer-facing AI solutions from solution design and model development through deployment and post-production optimization.
- Experience across the full product development lifecycle, including requirements, estimation, design, implementation, testing, deployment, and production support.
- Strong problem-solving skills and the ability to think outside the box.
- Preferred experience includes enterprise AI platforms, scalable high-performance services, LLMs, prompt engineering, RAG systems, GPU optimization, ONNX, TensorRT, model compression, compliance frameworks, reinforcement learning, multimodal systems, and AI transformation leadership.
- Publications, patents, or open-source contributions in AI/ML are preferred.
Tech Stack
Categories