AI/ML Engineer - Python

Job Title : AI-ML Engineer with Python

Experience : 5- 8 Years

Location : Hyderabad (Preferred)

Employment Type : Full-Time

Job Summary :

We are looking for an experienced Python Data Engineer with strong exposure to AI/ML and cloud data platforms to build and scale robust data pipelines and analytics solutions. The ideal candidate should have hands-on experience in real-time data processing, AWS cloud ecosystem, and MLOps practices, along with a strong focus on performance, scalability, and data security.

Key Responsibilities :

- Design and implement scalable data pipelines and ETL/ELT workflows for large-scale data processing.

- Build and manage real-time and batch data processing systems using tools like Kafka, Spark, or Flink.

- Develop and optimize cloud-native data solutions on AWS (S3, Lambda, Glue, Kinesis, RDS, MSK, etc.).

- Implement and manage data orchestration frameworks such as Airflow, DBT, or Dagster.

- Collaborate with data science teams to support ML model deployment and MLOps pipelines.

- Develop REST APIs and backend services using Python (FastAPI/Django).

- Ensure data quality, governance, and compliance standards (e.g., PCI DSS).

- Implement CI/CD pipelines and automation using Jenkins, ArgoCD, or similar tools.

- Monitor, troubleshoot, and optimize data workflows for performance and reliability.

- Work closely with cross-functional teams in an Agile/Scrum environment.

Required Skills (Must Have) :

- Strong hands-on experience in Python programming

- Expertise in Data Engineering concepts (ETL/ELT, Data Pipelines, Data Warehousing)

- Solid experience with AWS Cloud Services (S3, Lambda, Glue, Kinesis, RDS, etc.)

- Strong knowledge of SQL and database systems (PostgreSQL, MySQL, NoSQL)

- Experience with Big Data technologies (Kafka, Spark, Flink)

- Hands-on with workflow orchestration tools (Airflow / DBT / Dagster)

- Experience in API development (FastAPI/Django)

- Understanding of CI/CD pipelines and DevOps practices

Good to Have Skills :

- Exposure to MLOps tools (Kubeflow, MLflow)

- Experience in real-time analytics or fraud detection systems

- Knowledge of Docker, Kubernetes, Terraform (IaC)

- Experience with data visualization tools (Tableau, QuickSight)

- Familiarity with data security and compliance standards (PCI DSS)

Qualifications :

- Bachelor's degree in Computer Science, Engineering, or related field

- AWS Certification (Preferred)