AI-ML Data Engineer

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.
• 5+ 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.