AI Research Engineer – Private LLM & Cognitive Systems

4 years

0 Lacs

Posted:2 weeks ago| Platform: Linkedin logo

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Remote

Job Type

Full Time

Job Description

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About Brainwave Science:


Brainwave Science


About the Role


AI/ML Engineer – LLM & Deep Learning Expert


real-world challenges


Responsibilities

  • Design, train, fine-tune, and deploy

    Large Language Models

    using frameworks like

    PyTorch

    ,

    TensorFlow

    , or

    Hugging Face Transformers

     
  • Integrate LLMs for

    local/edge deployment

    using tools like

    Ollama

    ,

    LangChain

    ,

    LM Studio

    , or

    llama.cpp

     
  • Build NLP applications for

    intelligent automation

    ,

    investigative analytics

    , and

    biometric interpretation

     
  • Optimize models for

    low-latency

    ,

    token efficiency

    , and

    on-device performance

     
  • Work on

    prompt engineering

    ,

    embedding tuning

    , and

    vector search integration

    (FAISS, Qdrant, Weaviate) 
  • Collaborate with technical and research teams to deliver scalable AI-driven features 
  • Stay current with developments in

    open-source

    and

    closed-source

    LLM ecosystems (e.g., Meta, OpenAI, Mistral) 


Must-Have Requirements


  • B.Tech/M.Tech

    in

    Computer Science (CSE)

    ,

    Electronics & Communication (ECE)

    , or

    Electrical & Electronics (EEE)

    from

    IIT, NIT, or BITS

     
  • Minimum

    3

    -

    4 years of hands-on experience

    in

    AI/ML, deep learning

    , and

    LLM development

     
  • Deep experience with

    Transformer models

    (e.g., GPT, LLaMA, Mistral, Falcon, Claude) 
  • Hands-on with tools like

    LangChain

    ,

    Hugging Face

    ,

    Ollama

    ,

    Docker

    , or

    Kubernetes

     
  • Proficiency in

    Python

    , and strong knowledge of

    Linux environments

     
  • Strong understanding of

    NLP

    ,

    attention mechanisms

    , and

    model fine-tuning

     

Preferred Qualifications

  • Experience with

    biosignals

    , especially

    EEG

    or

    time-series data

     
  • Experience deploying custom-trained LLMs on

    proprietary datasets

     
  • Familiarity with

    RAG pipelines

    and

    multi-modal models

    (e.g., CLIP, LLaVA) 
  • Knowledge of

    cloud platforms

    (AWS, GCP, Azure) for scalable model training and serving 
  • Published research, patents, or open-source contributions in AI/ML communities 
  • Excellent communication, analytical, and problem-solving skills 

 


What We Offer

  • Competitive compensation based on experience 
  • Flexible working hours and remote-friendly culture 
  • Access to high-performance compute infrastructure 
  • Opportunities to work on groundbreaking AI projects in healthcare, security, and defense 

 

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