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Open nowPosted 468 days ago

AI & ML Engineer

FACTENTRY15 open roles

Where
Vellore
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Your applicationOpen nowAI & ML EngineerFACTENTRY · Vellore

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Share of postings closed within
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  2. 4.4%3 days
  3. 11.9%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 468 days ago

The posting

Job Summary

We are looking for an experienced AI & Generative AI Developer who can work across the AI spectrum—from classical machine learning models to cutting-edge Generative AI applications. The role demands strong experience in building ML models using regression, classification, and tree-based algorithms, along with hands-on exposure to LLMs and generative frameworks like GPT, Stable Diffusion, and LangChain.

Key Responsibilities

🔹 Classical AI/ML

  • Design and implement supervised and unsupervised ML models including:
  • Linear Regression, Logistic Regression
  • Decision Trees, Random Forest, XGBoost
  • Naive Bayes, K-Means, SVM, PCA, etc.
  • Preprocess and analyse structured/tabular datasets
  • Evaluate models using metrics like accuracy, precision, recall, ROC-AUC, and RMSE
  • Build predictive models, deploy them into production, and monitor performance
  • Collaborate with business teams to translate requirements into ML use cases

🔹 Generative AI (GenAI)

  • Build and fine-tune LLMs (e.g., GPT, LLaMA, PaLM) for summarisation, Q&A, document generation, etc.
  • Implement prompt engineering, RAG pipelines, and vector database integrations
  • Use libraries like Hugging Face Transformers, LangChain, and LlamaIndex
  • Develop APIs to expose GenAI models in real-time apps
  • Optimise model inference using quantisation, batching, etc.
  • Ensure safe, explainable, and bias-free output in alignment with AI ethics guidelines

Required Skills & Qualifications

  • Bachelor’s or Master’s in Computer Science, Data Science, Statistics, or related field
  • Strong programming skills in Python, with experience in NumPy, Pandas, Scikit-learn
  • Proficiency in classical ML algorithms (regression, trees, naive Bayes, etc.)
  • Experience with LLM frameworks like OpenAI API, Hugging Face, and LangChain
  • Understanding of transformer architecture, NLP, embeddings, and tokenisation
  • Familiarity with REST API development using FastAPI/Flask
  • Exposure to cloud platforms (AWS/GCP/Azure) and Docker/Kubernetes

Preferred / Nice to Have

  • Experience with deep learning (TensorFlow, PyTorch)
  • Exposure to image/audio/video generation using models like DALL·E, Stable Diffusion, Whisper
  • Familiarity with RAG, LLMOps, and vector stores (FAISS, Pinecone, Weaviate)
  • Knowledge of MLOps pipelines, model monitoring, and CI/CD for ML
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