Python-ML-MLOps Professional

TymblHub; India; On-site; INR 800,000 - 1,200,000; Full time

Job summary

TymblHub is seeking an experienced Data Analytics professional to join the dedicated Data Analytics Unit. The role focuses on Python-based data and ML applications, building and managing ML pipelines, and applying strong ML concepts including NLP, CV, and deep learning.

Candidates should demonstrate hands-on ML tooling experience, REST API work, and proficiency with SQL/NoSQL databases in cloud environments like AWS, Azure, or GCP.

Qualifications

• Experience in Python development for data/ML applications.
• Strong understanding of ML algorithms (supervised, unsupervised, NLP, etc.).
• Hands-on experience with ML libraries/tools listed.
• Experience with REST APIs (FastAPI/Flask).
• Strong knowledge of data structures, algorithms, and statistics.
• Experience with SQL and NoSQL databases.

Responsibilities

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

Skills

IoT

IoT Analytics

Machine Learning

Python

Education

BTech

BCA

BSc

MCA

MTech

MSc

Tools

Scikit-learn

TensorFlow

PyTorch

XGBoost

MLflow

DVC

Kubeflow

Airflow

SageMaker

Vertex AI

Docker

Kubernetes

Spark

Hadoop

Job description

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