Sr.Data Engineer MAHIN-JOB-34276

Keylent, Inc.

$120K — $130K *
Information Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • 7+ years of experience as a data engineer
  • Deep expertise in distributed computing frameworks (e.g., Apache Spark, Hadoop, Flink)
  • Proficient in Python and familiar with data structures and algorithms
  • Experience with AWS, Google Cloud, or Azure for data storage and processing
  • Knowledge of ETL orchestration tools (e.g., Apache Airflow)

Responsibilities

  • Design and manage scalable data pipelines for large language model fine-tuning
  • Develop and optimize data storage architectures for massive data volumes
  • Implement data preprocessing and feature extraction techniques
  • Collaborate with engineers and researchers for data requirements
  • Design robust systems for data ingestion and processing
  • Optimize data pipelines for performance and cost-efficiency
  • Ensure data security and compliance with industry standards

Benefits

  • Day one onsite work
  • Opportunities to collaborate with AI system engineers
  • Impactful role in advancing large language models
  • Involvement in cutting-edge data engineering projects
Full Job Description
Sr.Data Engineer MAHIN-JOB-34276
We are looking for Data Engineer in Austin,TX. ( I'm not looking for Banking or Insurance co. experienced profiles. Please submit only product base co. experienced profiles)

Day1 onsite


MUST to have Skills -
  • Python
  • Pyspark
  • Bigdata
  • SQL
  • Data engineering
Full time -$120-130k per annum based on the year of experience
Subcon Rate - 70-75$ based on the years of experience - No layers are accepted

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 the exciting opportunity to collaborate closely with applied

Client engineers, software engineers, and data scientists that 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 Prefect 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 effectiv

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