Python-ML-MLOps Professional

Educational Requirements

• BTech
• BCA
• BSc
• MCA
• MTech
• MSc

Service Line

Data Analytics Unit

Responsibilities

• 35 years of experience in Python development for data/ML applications.
• Strong understanding of Machine Learning algorithms (supervised, unsupervised, NLP, etc.).
• Hands-on experience with ML libraries/tools: Scikit-learn, TensorFlow, PyTorch, XGBoost
• Experience in building and managing ML pipelines.
• Solid knowledge of MLOps concepts: Model versioning (MLflow, DVC), CI/CD pipelines
• Model deployment monitoring
• Experience with REST APIs (FastAPI/Flask).
• Strong knowledge of data structures, algorithms, and statistics.
• Experience with SQL and NoSQL databases.

Additional Responsibilities

• Experience with cloud platforms (AWS, Azure, or GCP).
• Hands-on with MLOps tools: MLflow, Kubeflow, Airflow, SageMaker, Vertex AI
• Knowledge of Docker Kubernetes.
• Experience with big data tools (Spark, Hadoop).
• NLP, Computer Vision, or Deep Learning specialization.
• Familiarity with Data Engineering pipelines.

Technical and Professional Requirements

• Primary skills: Technology- >Internet of Things (IOT)- >IOT Analytics - Machine Learning, Technology- >Machine Learning- >Python

Preferred Skills

• Technology- >AI-Data science- >PYTHON
• Technology- >AI-Physical AI-IOT- >IOT Analytics - Machine Learning