Data Scientist

Wibix Consulting

2 years

0 Lacs

Gurugram Haryana India

Posted:18 hours ago| Platform:

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Skills Required

data ai design ml power engineering model deployment training preprocessing checks tracking inference docker mlflow sagemaker vertex versioning drift rest aws gcp azure storage automate provisioning terraform pipeline python sql processing spark orchestration kubernetes tensorflow pytorch api development flask fastapi airflow devops communication collaboration

Work Mode

Remote

Job Type

Full Time

Job Description

Location:

Department:

Employment Type:

Experience Level:

About the Role:

AI Data Engineer

Key Responsibilities:

Data Engineering & Development

Design, develop, and train AI models to solve complex business problems and enable intelligent automation.
Design, develop, and maintain scalable data pipelines and workflows for AI/ML applications.
Ingest, clean, and transform large volumes of structured and unstructured data from diverse sources (APIs, streaming, databases, flat files).
Build and manage data lakes, data warehouses, and feature stores.
Prepare training datasets and implement data preprocessing logic.
Perform data quality checks, validation, lineage tracking, and schema versioning.

Model Deployment & MLOps

Package and deploy AI/ML models to production using CI/CD workflows.
Implement model inference pipelines (batch or real-time) using containerized environments (Docker, Kubernetes).
Use MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI) for model tracking, versioning, and deployment.
Monitor deployed models for performance, drift, and reliability.
Integrate deployed models into applications and APIs (e.g., REST endpoints).

Platform & Cloud Engineering

Manage cloud-based infrastructure (AWS, GCP, or Azure) for data storage, compute, and ML services.
Automate infrastructure provisioning using tools like Terraform or CloudFormation.
Optimize pipeline performance and resource utilization for cost-effectiveness.

Requirements:

Must-Have Skills

Bachelor's/Master’s in Computer Science, Engineering, or related field.
2+ years of experience in data engineering, ML engineering, or backend infrastructure.
Proficient in Python, SQL, and data processing frameworks (e.g., Spark, Pandas).
Experience with cloud platforms (AWS/GCP/Azure) and services like S3, BigQuery, Lambda, or Databricks.
Hands-on experience with CI/CD, Docker, and container orchestration (Kubernetes, ECS, EKS).

Preferred Skills

Experience deploying ML models using frameworks like TensorFlow, PyTorch, or Scikit-learn.
Familiarity with API development (Flask/FastAPI) for serving models.
Experience with Airflow, Prefect, or Dagster for orchestrating pipelines.
Understanding of DevOps and MLOps best practices.

Soft Skills:

Strong communication and collaboration with cross-functional teams.
Proactive problem-solving attitude and ownership mindset.
Ability to document and communicate technical concepts clearly.

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