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.
Skills
MLops, Model Deployment, Model Monitoring, Model Fine tuning, Sagemaker, Vertex