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Senior Software Development Engineer (AI/ML and GenAI Engineer)

Diebold Nixdorf -
Mumbai, Maharashtra

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Job details

Full-time

Qualifications

TensorFlow
Image processing
Azure
Law
PyTorch
Computer vision
Engineering
NumPy
M.Eng
Master's degree
SQL
Pandas
AWS
Bachelor's degree
Product development
Machine learning
Deep learning
Natural language processing
Software development
Agile
Keras
AI
Python

Full job description

The Senior Software Engineer role is an individual contributor position within the software product development organization, responsible for understanding requirements thoroughly, identifying and bridging gaps, and designing, implementing, and testing end ‑ to ‑ end AI-driven solutions. We are seeking an experienced AI/ML and GenAI Engineer to lead the design, development, and deployment of advanced machine learning and generative AI solutions. You will play a key role in architecting scalable AI systems, including LLM ‑ based and agentic workflows, mentoring junior engineers, and driving AI initiatives from concept to production.

Responsible for

End ‑ to ‑ End AI Solution Development: Architect, implement, and optimize AI/ML models for NLP, computer vision, predictive analytics, generative AI, and agentic AI workflows.
Build robust, scalable pipelines for data ingestion, model training, evaluation, and inference, following best practices in MLOps and AI lifecycle management.
Deploy models to production environments (cloud/on-prem), ensure performance, reliability, security and compliance.
Mentor junior engineers, review code, and contribute to architectural decisions.
Work closely with product managers, data scientists, and DevOps teams to integrate AI into business applications.
Stay updated with emerging AI technologies and frameworks; evaluate and implement new approaches.
Ensures timely delivery and excellent quality of the deliverables for the product
Working on low latency high performance systems
Able to work in agile software development environment
Acts as subject matter expert for the domain, processes, methodology and able to adapt latest technologies, frameworks and tools quickly

Required Qualifications

4.5–8 years of experience in software engineering, with at least 4+ years focused on AI/ML development, including hands ‑ on exposure to generative AI, LLM-based systems, and agentic AI architectures.
Full ‑ time Bachelor ’ s and/or Master ’ s degree in Engineering with a minimum of 60% grade.
Strong programming proficiency in Python, with experience developing production-grade AI/ML and GenAI applications.
ML Frameworks: Hands ‑ on experience with TensorFlow, PyTorch, scikit ‑ learn, Keras, along with experience in LLM/GenAI and agentic AI frameworks such as Hugging Face Transformers, LangChain, or LangGraph/CrewAI/Autogen.
Proficiency in Machine Learning, Deep Learning, Natural Language Processing, Generative AI, and Agentic AI concepts such as tool ‑ use, multi ‑ agent orchestration, and reasoning workflows.
Experience developing, fine ‑ tuning, optimizing, and deploying AI/ML, LLM, and agentic AI models across cloud or on ‑ prem environments.
Knowledge of data processing, feature engineering, and visualization tools (e.g., Pandas, NumPy, SQL).
Ability to work effectively in global, cross ‑ functional teams and manage multiple concurrent deliverables.
Demonstrated experience across the end ‑ to ‑ end product development lifecycle: design, implementation, testing, debugging, deployment, and maintenance of AI systems.
Strong problem ‑ solving skills, analytical thinking, and the ability to innovate and adopt new AI, GenAI, and agentic AI technologies.
Preferred Qualifications
Experience in delivering scalable, high ‑ performance AI services and applications with excellent quality across cloud or on ‑ prem environments.
Experience with Large Language Models (LLMs), prompt engineering, retrieval ‑ augmented generation (RAG), and developing agentic AI workflows using frameworks such as LangChain, LangGraph/Crew AI/AutoGen.
Familiarity with computer vision, NLP, generative AI frameworks, and multimodal model development.
Exposure to MLOps tools and optimization technologies such as MLflow, Triton, ONNX, TensorRT, model quantization, GPU acceleration, or distributed inference.
Familiarity with cloud ‑ native deployments on AWS, GCP, or Azure, including experience integrating AI/ML and LLM systems into secure enterprise environments.

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Senior Software Development Engineer (AI/ML and GenAI Engineer)

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