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Artificial Intelligence / Machine Learning Engineer

Artificial Intelligence / Machine Learning Engineer

MOBCODER TECHNOLOGIES PRIVATE LIMITEDNoida
30+ days ago
Job description

Title : AI / ML Engineer

Location : Sector 63, Noida

About the Role :

We are seeking a talented and hands-on AI / ML Engineer with experience in LLM-based architectures, vector search (e.g., Pinecone), and end-to-end model deployment. You'll be working closely with our product and research teams to develop scalable NLP / NLU applications, including RAG pipelines, LLM integrations, and custom model deployments.

Key Responsibilities :

  • Design and implement Retrieval-Augmented Generation (RAG) pipelines using LLMs and vector databases like Pinecone.
  • Integrate with OpenAI, LLaMA, and Hugging Face models to build conversational AI solutions.
  • Work with vector databases (e.g., Pinecone, Weaviate, FAISS) for embedding-based retrieval.
  • Fine-tune and serve LLMs (LLaMA, GPT, etc.) locally or via cloud deployments.
  • Implement NLP / NLU tasks including summarization, classification, entity extraction, etc.
  • Build and deploy ML pipelines using TensorFlow or PyTorch (preferred but not mandatory).
  • Perform model evaluations, optimizations, and monitor post-deployment performance.
  • Collaborate with backend and DevOps teams to deploy models using Docker, FastAPI, or other modern tools.

Required Skills :

  • 3+ years of experience in AI / ML or Data Science roles.
  • Strong experience with LLMs (e.g., GPT-4, LLaMA, Falcon).
  • Hands-on experience with RAG architectures and embedding pipelines.
  • Familiarity with OpenAI APIs, LangChain, or LLM tooling frameworks.
  • Working knowledge of vector stores like Pinecone, FAISS, or Weaviate.
  • Proficient in Python and libraries like transformers, scikit-learn, spaCy, etc.
  • Exposure to model serving & deployment FastAPI, Flask, Docker, TorchServe, etc.
  • Familiarity with NLP / ML lifecycle from training to inference and monitoring.

    • Good to Have :

  • Experience with TensorFlow or PyTorch.
  • Experience in deploying LLMs locally (LLaMA with llama.cpp or Ollama).
  • Experience in managing Hugging Face Spaces, datasets, or model hub.
  • MLOps experience : CI / CD, model versioning, cloud deployment.

    • (ref : hirist.tech)

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