Senior Software Engineer (MLOPS Engineer)

As a Senior MLOps Engineer, you will collaborate with Data Scientists, Software Engineers, Product Owners, and Architects to design, build, deploy, and operate scalable AI/ML solutions in production. You will drive the adoption of engineering best practices, CI/CD automation, and cloud-native MLOps capabilities while enabling rapid delivery of Machine Learning and Generative AI solutions. Key Responsibilities Design and maintain scalable MLOps infrastructure and platforms for AI/ML workloads. Develop and maintain RESTful APIs and backend services using Python. Build and automate ML training, deployment, monitoring, and retraining pipelines using Azure ML. Design and implement CI/CD pipelines using GitHub Actions and related DevOps tools. Deploy and manage containerized workloads using Docker and Kubernetes. Implement monitoring, logging, alerting, and observability solutions for applications and ML models. Develop and support RAG (Retrieval-Augmented Generation) solutions, including embedding generation, semantic search, and vector retrieval. Integrate and optimize Vector Database technologies such as Azure AI Search, Pinecone, Weaviate, Chroma, Milvus, or equivalent platforms. Partner with Data Scientists to operationalize ML, LLM, and Generative AI solutions. Troubleshoot production issues involving applications, infrastructure, data pipelines, and ML models. Contribute to architecture design, scalability planning, and performance optimization. Drive engineering excellence through code reviews, automated testing, coding standards, and secure development practices. Establish best practices for unit testing, integration testing, performance testing, and release management. Mentor engineers and promote modern software engineering and MLOps practices. Evaluate new technologies, frameworks, and tools to improve platform capabilities. Create and maintain technical documentation, architecture diagrams, and operational runbooks.

6+ years of experience in Software Engineering, Data Engineering, Platform Engineering, or MLOps. Strong hands-on expertise in Python and REST API development (FastAPI/Flask). Experience building RAG pipelines and LLM-powered applications. Hands-on experience with embedding models and semantic search architectures. Experience building and operating ML workflows using Azure ML or similar platforms. Strong understanding of data structures, distributed systems, scalability, reliability, and performance optimization. Experience with Pandas, NumPy, SQLAlchemy, and data processing frameworks. Strong SQL and database experience across relational and NoSQL technologies. Experience with Docker, Kubernetes (AKS preferred), and Linux environments. Experience implementing CI/CD pipelines using GitHub Actions, Azure DevOps, Jenkins, or similar tools. Hands-on experience with Git, GitHub workflows, branching strategies, and repository management. Experience with monitoring and observability tools such as Azure Monitor, Application Insights, Grafana, or Prometheus. Experience with cloud platforms, preferably Microsoft Azure. Strong understanding of messaging and event-driven architectures such as Kafka or Service Bus

Enjoy a flexible and rewarding work environment with peer-to-peer recognition platforms. Recharge and revitalize with help of wellness plans made for you and your family. Plan your future with financial wellness tools. Stay relevant and upskill yourself with career development opportunities. Our Benefits Flexible working environment Volunteer time off LinkedIn Learning Employee-Assistance-Program (EAP) NIQ may utilize artificial intelligence (AI) tools at various stages of the recruitment process, including résumé screening, candidate assessments, interview scheduling, job matching, communication support, and certain administrative tasks that help streamline workflows. These tools are intended to improve efficiency and support fair and consistent evaluation based on job-related criteria. All use of AI is gov