Senior ML Ops Engineer

Job Description

We are looking for a Machine Learning Engineer with expertise in MLOps to develop, deploy, and maintain scalable AI/ML pipelines . This role requires a strong background in machine learning, model deployment, cloud platforms, and automation to ensure seamless integration of AI models into production systems. You will work closely with data scientists, engineers, and DevOps teams to optimize model performance and reliability. Key Responsibilities Model Development & Deployment Design, build, and deploy scalable and efficient ML models for production use. Implement CI/CD pipelines for ML workflows using GitHub Actions, Jenkins, or GitLab CI/CD . Optimize model inference using ONNX, TensorRT, or TorchScript for faster deployment. Work with MLOps tools like MLflow, Kubeflow, TFX, and SageMaker . MLOps & Model Monitoring Develop and maintain end-to-end ML pipelines , including data ingestion, preprocessing, training, and deployment. Implement model versioning, monitoring, and retraining strategies . Set up automated model performance tracking and real-time anomaly detection using Prometheus, Grafana, or Weights & Biases. Cloud & Infrastructure Deploy models on AWS, GCP, or Azure using services like SageMaker, Vertex AI, or Azure ML . Work with containerization (Docker, Kubernetes) for model serving. Manage serverless AI deployments using Lambda, Cloud Run, or Azure Functions . Collaboration & Best Practices Work with data scientists, software engineers, and DevOps teams to optimize model integration. Establish MLOps best practices , including feature stores, automated testing, and data versioning . Ensure compliance with AI governance, model explainability, and security best practices . Qualifications & Experience Education: Bachelor's/Master s in Computer Science, AI/ML, Data Engineering, or a related field . Experience: 5-8 years in ML engineering, model deployment, and MLOps . Technical Expertise: Strong Python skills ( FastAPI, Flask, PyTorch, TensorFlow, Scikit-learn ). Experience with Kubernetes, Docker, Terraform, and cloud-based AI services . Knowledge of vector databases (FAISS, Pinecone), data pipelines (Airflow, Prefect), and streaming (Kafka, Spark Streaming) . Soft Skills: Strong problem-solving and debugging skills. Ability to work in cross-functional teams and handle production ML challenges. Strong communication and documentation abilities. Nice-to-Have: Experience with Generative AI and LLMOps (e. g. , Hugging Face, LangChain, LlamaIndex). Background in edge AI deployments or real-time AI applications . Contributions to open-source AI/ML projects .