Factentry·14 months ago
14 months ago
AI & ML Engineer
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About this role
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
Company
Factentry
Vellore, India
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