AI and Data Engineer

Full Description

We are seeking a motivated AI & Data Engineer with 2–3 years of experience to join our innovative team.

This role sits at the intersection of data engineering, cloud analytics, and applied AI, where you’ll design scalable data pipelines while also enabling LLM-powered applications and machine learning workflows.

You will collaborate closely with data scientists, ML engineers, and product teams totransform raw data into intelligent insights, AI-driven features, and production-ready LLM solutions.

Key Responsibilities

Data Engineering & Analytics

Design, build, and optimize ETL pipelines using Apache Airflow

Build and maintain Snowflake data warehouses for scalable ingestion, transformation, and analytics

Develop data workflows for structured and semi-structured data using AWS services

Integrate data from APIs, databases, and cloud storage using Python

Implement data quality checks, monitoring, and validation frameworks

Design and document data models to support analytics and ML workflows

Document pipeline architectures, schemas, and best practices

AI, ML & LLM Enablement

Collaborate with data scientists and ML engineers to prepare datasets for AI/ML training and inference

Support LLM-powered applications, including:

Prompt engineering and optimization

Fine-tuning LLMs (instruction tuning, domain adaptation)

Managing embeddings, vector databases, and retrieval pipelines

Build data pipelines to support RAG (Retrieval-Augmented Generation) workflows

Develop Streamlit applications for data exploration, AI demos, and internal tools

Assist in deploying and monitoring AI/ML pipelines in production environments

Required Qualifications

2–3 years of professional experience in Data Engineering, AI Engineering, or a related role

Strong hands-on experience with Apache Airflow

Practical experience with AWS services (S3, Lambda, Glue, EC2, or similar)

Experience working with Snowflake for data warehousing

Strong Python skills for data processing, automation, and API integration

Experience building data apps or dashboards using Streamlit

Solid understanding of data modeling and ETL design

Working knowledge of AI/ML workflows, especially data preparation

Familiarity with LLMs, including:

Prompt engineering

Fine-tuning or adapting LLMs

Working with embeddings and vector stores

Bachelor’s degree in Computer Science, Data Engineering, AI, or equivalent experience

Preferred Qualifications

Advanced experience with AWS data and ML services

Deep expertise in Snowflake performance tuning and optimization

Experience building complex Streamlit applications

Advanced Python skills for large-scale data wrangling

Experience with LLM fine-tuning, evaluation, and deployment

Familiarity with LangChain, LlamaIndex, or similar LLM frameworks

Knowledge of Airflow best practices for scalable pipeline design

Experience with AI/ML model deployment and monitoring