Machine Learning Engineer

Join NICE's AI Engineering team as a Machine Learning Engineer in Pune, India, to build and operationalize cutting-edge AI/ML and Generative AI solutions. This role focuses on designing and implementing robust software systems for MLOps and GenAI Ops workflows, ensuring scalable, secure, and efficient deployment of AI models and applications. You will collaborate with data scientists, prompt engineers, and cloud specialists to deliver production-grade AI services utilizing Azure AI and AI Foundry.

Key Responsibilities:

• Design and implement GenAI Ops pipelines for LLM deployment, fine-tuning, and inference optimization.
• Automate prompt lifecycle management, versioning, and evaluation.
• Integrate Azure AI services and AI Foundry for scalable GenAI solutions.
• Build CI/CD workflows for LLM-based applications, including containerization and orchestration.
• Implement observability and monitoring for LLM performance, latency, and hallucination detection.
• Collaborate with Data Scientists and Prompt Engineers to streamline GenAI workflows.
• Ensure compliance with Responsible AI, data privacy, and model governance.

Required Skills and Qualifications:

• Experience: 2-4 years in relevant ML/GenAI operations.
• Cloud Platforms: Strong experience with Azure AI, Azure ML, and Azure DevOps.
• GenAI Tools: Hands-on with AI Foundry, OpenAI/Azure OpenAI APIs, or similar platforms.
• Programming: Proficiency in .Net or Python; experience with prompt engineering frameworks.
• CI/CD: Familiarity with Azure DevOps pipelines, Docker, Kubernetes.
• Monitoring: Knowledge of LLM observability tools and metrics for GenAI.
• Understanding of MLOps principles and GenAI Ops best practices.
• Academic Background: Bachelor's or master's degree in computer science or a related field.
• Additional Experience: Fine-tuning LLMs and embedding models, knowledge of vector databases (e.g., PostgressPGE Vector, Azure Cognitive Search), and familiarity with RAG architectures.

NICE offers a dynamic and collaborative environment with opportunities for continuous learning and career growth. We embrace a hybrid work model (NiCE-FLEX) with 2 days in the office and 3 days remote work per week.