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