AI-ML Data Engineer

CoreFlex Solutions Inc.; Maharashtra; On-site; INR 900,000 - 1,500,000; Full time

Job summary

CoreFlex Solutions Inc. is seeking a data engineer / machine learning engineer to design, build, and maintain data pipelines and ML models for structured and unstructured data.

You will collaborate with data engineers, analysts, and product managers to translate analytics requirements into production-ready models and APIs. You will optimize data processing and model inference for scalable, cost-efficient cloud deployment on AWS/Azure/GCP, and enable analytics dashboards with BI teams.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or related field.
• 2+ years of hands-on experience in Data Engineering, Machine Learning, or AI solution development.
• Proficiency in Python, SQL, and data manipulation libraries such as Pandas, NumPy, or PySpark.
• Experience with data pipelines using Airflow, Databricks, AWS Glue, or Apache Spark.
• Strong understanding of ML algorithms (supervised, unsupervised, NLP, deep learning) and experience with at least one deployment-ready framework (TensorFlow /PyTorch).
• Experience deploying models through REST APIs, containers (Docker, Kubernetes), or serverless platforms (AWS Lambda, Azure Functions).
• Familiarity with cloud-based data platforms – AWS (S3, Redshift, SageMaker), Azure Synapse, or GCP (BigQuery, Vertex AI).
• Understanding of data governance, metadata management, and data security best practices.
• Strong communication and collaboration skills, capable of engaging with both business and technical teams.

Responsibilities

• Design, build, and maintain data pipelines and ETL/ELT workflows for structured and unstructured data sources.
• Develop, train, validate, and deploy machine learning and AI models using frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Collaborate with data engineers, business analysts, and product managers to translate analytical requirements into production-grade models and APIs.
• Optimize data processing performance and model inference pipelines for scalability and cost-efficiency (preferably on cloud platforms such as AWS, Azure, or GCP).
• Work with data visualization and BI teams to enable analytics dashboards that surface insights from AI models.
• Ensure data quality, governance, and compliance with organizational and regulatory standards.
• Communicate complex technical outcomes to non-technical stakeholders with clarity and impact.

Skills

Python

SQL

Pandas

NumPy

PySpark

Airflow

Databricks

AWS Glue

Apache Spark

TensorFlow

PyTorch

REST APIs

Docker

Kubernetes

AWS

Education

BSc/MSc in CS/DS/Statistics

Tools

Airflow

Databricks

AWS Glue

Apache Spark

Docker

Kubernetes

AWS Lambda

Synapse

Vertex AI

BigQuery

Job description

• Design, build, and maintain data pipelines and ETL/ELT workflows for structured and unstructured data sources.
• Develop, train, validate, and deploy machine learning and AI models using frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Collaborate with data engineers, business analysts, and product managers to translate analytical requirements into production-grade models and APIs.
• Optimize data processing performance and model inference pipelines for scalability and cost-efficiency (preferably on cloud platforms such as AWS, Azure, or GCP).
• Work with data visualization and BI teams to enable analytics dashboards that surface insights from AI models.
• Ensure data quality, governance, and compliance with organizational and regulatory standards.
• Communicate complex technical outcomes to non-technical stakeholders with clarity and impact.

Job Description

Roles & Responsibilities

• Design, build, and maintain data pipelines and ETL/ELT workflows for structured and unstructured data sources.
• Develop, train, validate, and deploy machine learning and AI models using frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Collaborate with data engineers, business analysts, and product managers to translate analytical requirements into production-grade models and APIs.
• Optimize data processing performance and model inference pipelines for scalability and cost-efficiency (preferably on cloud platforms such as AWS, Azure, or GCP).
• Work with data visualization and BI teams to enable analytics dashboards that surface insights from AI models.
• Ensure data quality, governance, and compliance with organizational and regulatory standards.
• Communicate complex technical outcomes to non-technical stakeholders with clarity and impact.

Required Skills And Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or related field.
• 2+ years of hands-on experience in Data Engineering, Machine Learning, or AI solution development.
• Proficiency in Python, SQL, and data manipulation libraries such as Pandas, NumPy, or PySpark.
• Experience with data pipelines using Airflow, Databricks, AWS Glue, or Apache Spark.
• Strong understanding of ML algorithms (supervised, unsupervised, NLP, deep learning) and experience with at least one deployment-ready framework (TensorFlow /PyTorch).
• Experience deploying models through REST APIs, containers (Docker, Kubernetes), or serverless platforms (AWS Lambda, Azure Functions).
• Familiarity with cloud-based data platforms – AWS (S3, Redshift, SageMaker),Azure (Synapse, ML Studio), or GCP (BigQuery, Vertex AI).
• Understanding of data governance, metadata management, and data security best practices.
• Strong communication and collaboration skills, capable of engaging with both business and technical teams.
• Proven ability to deliver in agile environments and manage competing priorities effectively.

Preferred / Good-to-Have Skills

• Experience with LLMs, Generative AI, or prompt engineering for text/image applications.
• Knowledge of data versioning (DVC) and feature stores for ML pipelines.
• Exposure to data visualization tools (Tableau, Power BI, Looker).
• Understanding of DevOps/MLOps CI/CD for model deployment.
• Certification in AWS AI/ML Specialty, Azure Data Scientist, or Google Cloud ML Engineer.

Key Attributes

• Strong analytical mindset with attention to detail.
• Curious and self-driven to explore emerging AI technologies.
• Effective communicator who can explain data and model insights to leadership.
• Team player with a focus on knowledge sharing and continuous learning.

Skills: azure,machine learning,mlops,llm,data engineering,gen ai