AI Research Engineer – Private LLM & Cognitive Systems

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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