Job Description

We are seeking Multiple Generative AI Engineers to drive the development of state-of-the art AI Technologies for our AI platform. Spearhead into the development of cutting-edge AI models, Platform & Architecture. You will be at the forefront of innovation, driving the evolution of our AI systems to enhance performance, scalability, and user experience.

Job Responsibilities

Responsibilities:

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  Develop and optimize RAG systems: Design, develop, and deploy Retrieval-Augmented Generation (RAG) systems utilizing dense (DPR) and sparse (BM25) retrieval mechanisms, embedding techniques, and Generative AI frameworks (LangChain, LlamaIndex).

  
  


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  NLP problem-solving and model performance: Solve complex NLP problems (text classification, entity tagging, question answering) and continually monitor model performance, conducting evaluations, and implementing improvements based on data analysis.

  
  


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  Data management and optimization: Ensure data quality and relevance through augmentation, noise removal, and optimization of retrieval efficiency. Evaluate query understanding techniques and implement context-aware retrieval strategies.

  
  


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  Evaluation and metrics: Define and implement evaluation metrics (relevance, factual accuracy, coherence) to assess RAG application effectiveness and monitor performance.

  
  


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  Model development and fine-tuning: Fine-tune and pre-train large language models (LLMs) using techniques like LoRA, QLORA, and prompt tuning. Integrate and optimize AI frameworks for seamless functionality and user experience.

  
  


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  Prompt engineering and research: Champion innovative prompt engineering to unlock zero-shot and few-shot learning capabilities. Lead research initiatives to explore new advancements in generative AI technology.

  
  

Collaboration and integration: Collaborate with cross-functional teams to integrate models into the platform and align with user needs.

Job Requirements

We're looking for someone with:

Educational Background: 

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  B.Tech/M.Tech in Computer Science, AI, or a related field.

  
  


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  1+ years of experience (strong advantage for peeps with Machine Learning, NLP, LLM hands-on experience)

  
  

Experience (1 to 2+ years ):

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  1+ Years of hands-on experience in Generative AI Projects.

  
  


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  Hands-on experience with Pre-Training/FineTuning, consuming foundational AI models via APIs.

  
  


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  Hands-on experience with RAG frameworks like (Langchain, LlamaIndex)

  
  


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   Hands on experience with Data Chunking, embedding approaches and developing Retrieval Augmented Generation solutions.

  
  


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  Hands on experience with LLMs context store development, vector search, prompt engineering

  
  


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  Hands-on experience with Prompt Engineering, In-Context Learning (zero-shot/few-shot learning)

  
  


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  Expert level understanding of Python & Hands-on experience with advanced AI/ML frameworks/Libraries (e.g., PyTorch, TensorFlow, Streamlit).

  
  


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  Hands-on experience with NLP, LLMs (Falcon, LLama, GPT series)

  
  


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  Hands-on experience with Vector Databases (Pinecone, Waviate, Faiss).

  
  


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  Hands on experience with building GenAI Web API REST services, FastAPI, Flask etc

  
  


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  Proficiency is backend service building and handling code at scale

  
  


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  Strong understanding of transformer architectures and model compression techniques.

  
  


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  Strong understanding of data preprocessing, feature engineering, and dataset curation for AI training

  
  

Bonus points if you have:

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  Participation in the AI and open-source communities is appreciated

  
  


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  Experience with model compression techniques, quantization, and efficient inference

  
  


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  Experience in fine tuning using techniques like PEFT, QLORA etc.

  
  


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  Knowledge of reinforcement learning and RLHF (Reinforcement Learning from Human Feedback) & Advance Prompt Engineering

  
  


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  A knack for data preprocessing, feature engineering, and dataset curation for AI training

  
  


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  Participation in AI and open-source communities.

  
  


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  A commitment to continuous learning and skill enhancement in AI technology.

  
  


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  Experience with cloud-based development and familiarity with AI-related cloud services (e.g, AWS, Azure, GCP).

  
  


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  Understanding of containerization and orchestration technologies (e.g, Docker, Kubernetes) for deploying AI services.

  
  

Essential Skills:

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  Exceptional problem-solving abilities and innovative thinking in AI research and development

  
  


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  Ability to balance cutting-edge research with practical implementation and user needs

  
  


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  Take ownership of your work in a high-ownership, high-commitment startup environment.

  
  

Interview Process

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  Qualification (Telephonic, Non-Technical, 10 Mins)

  
  


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  Technical Interview (45 Mins, Google Meet Video)

  
  


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  Coding Round (assignment code submission in 24 hours)

  
  


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  Final - Culture & Technical interview (60min, in-person)

  
  

docker

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