Role:Senior Data Engineer

Location:Austin,TX-onsite

Job Type: Full-time or Contract

Please share the resumes with me at pranayatburgeonitsdotcom

JD:

Must have Skills:

Python

Pyspark

Bigdata

SQL

Data engineering

We're seeking a Data Engineer to take the lead in implementing and scaling data

collection, storage, processing, and filtering for fine-tuning large language models (LLMs) within

Conversational Engineering. These data pipelines are crucial for powering our cutting-edge

research, safety systems, and product development. If you're passionate about working with

data and are eager to create solutions that directly impact the advancement of LLMs, we'd love

to hear from you. This role provides an exciting opportunity to collaborate closely with applied

ML engineers, software engineers, and data scientists create our AI systems today.

In this role, you will:

Design, build, and manage scalable data pipelines for collecting, storing, processing, and

filtering large volumes of text data for fine-tuning LLMs.

Develop and optimize data storage architectures to handle the massive scale of data

required for training state-of-the-art language models.

Implement efficient data preprocessing, cleaning, and feature extraction techniques to

ensure high-quality data for model training.

Collaborate with machine learning engineers and researchers to understand their data

requirements and provide tailored solutions for LLM fine-tuning.

Design and implement robust and fault-tolerant systems for data ingestion, processing,

and delivery.

Optimize data pipelines for performance, scalability, and cost-efficiency, leveraging

distributed computing frameworks and cloud platforms.

Ensure the security, privacy, and compliance of data according to industry best practices

and regulatory requirements.

You might thrive in this role if you:

Have 7 years of experience as a data engineer with a strong background in designing

and building large-scale data pipelines.

Possess deep expertise in distributed computing frameworks such as Apache Spark,

Hadoop, or Flink, and have hands-on experience optimizing data processing at scale.

Are proficient in programming languages commonly used in data engineering, such as

Python, and have a solid understanding of data structures and algorithms.

Have extensive experience with cloud platforms like AWS, Google Cloud, or Azure for

data storage, processing, and management.

Are well-versed in various data storage technologies, including distributed file systems

(e.g., HDFS, S3), databases (e.g., Cassandra, HBase), and data warehouses (e.g.,

Redshift, BigQuery).

Have hands-on experience with ETL orchestration tools such as Apache Airflow, Dagster,

or perfect for managing complex data workflows.

Possess knowledge of natural language processing (NLP) techniques and have worked

with text data preprocessing, normalization, and feature extraction.

Are passionate about staying up-to-date with the latest advancements in data

engineering and NLP, and are eager to apply innovative techniques to solve challenging

problems.

Have strong problem-solving skills, are detail-oriented, and can be effective