AI/ML Engineer

6 years

0 Lacs

Posted:1 week ago| Platform: Linkedin logo

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Work Mode

On-site

Job Type

Full Time

Job Description


Location

Salary Budget

Key Responsibilities

  • Design, build, and maintain

    scalable data pipelines

    (batch + streaming) using

    Spark

    ,

    Hadoop

    , and other Apache ecosystem tools.
  • Develop robust

    ETL workflows

    for large-scale data ingestion, transformation, and validation.
  • Work with

    Cassandra

    ,

    Data Lakes

    , and distributed storage systems to handle large-volume datasets.
  • Write clean, optimized, and modular

    Python

    code for data processing, automation, and machine learning tasks.
  • Utilize

    Linux

    environments for scripting, performance tuning, and data workflow orchestration.
  • Build and manage

    web scraping pipelines

    to extract structured and unstructured data from diverse sources.
  • Collaborate with ML/AI teams to prepare

    training datasets

    , manage feature stores, and support model lifecycle.
  • Implement and experiment with

    LLMs

    ,

    LangChain

    ,

    RAG pipelines

    , and vector database integrations.
  • Assist in

    fine-tuning models

    , evaluating model performance, and deploying ML models into production.
  • Optimize data workflows for performance, scalability, and fault tolerance.
  • Document data flows, transformation logic, and machine learning processes.
  • Work cross-functionally with engineering, product, and DevOps teams to ensure reliable, production-grade data systems.

Requirements

  • 3–6 years of experience

    as a

    Data Engineer

    ,

    ML Engineer

    , or similar role.
  • Strong expertise in

    Advanced Python

    (data structures, multiprocessing, async, clean architecture).
  • Solid experience with:
  • Apache Spark / PySpark

  • Hadoop ecosystem (HDFS, Hive, Yarn, HBase, etc.)

  • Cassandra

    or similar distributed databases
  • Linux

    (CLI tools, shell scripting, environment management)
  • Proven ability to design and implement

    ETL pipelines

    and scalable data processing systems.
  • Hands-on experience with

    data lakes

    , large-scale storage, and distributed systems.
  • Experience with

    web scraping frameworks

    (BeautifulSoup, Scrapy, Playwright, etc.).
  • Familiarity with

    LangChain

    ,

    LLMs

    ,

    RAG

    ,

    vector stores

    (FAISS, Pinecone, Milvus), and ML workflow tools.
  • Understanding of

    model training, fine-tuning, and evaluation

    workflows.
  • Strong problem-solving skills, ability to deep dive into complex data issues, and write production-ready code.
  • Experience with cloud environments (AWS/GCP/Azure) is a plus.

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