AI/ML Developer

LateShipment.com

2 - 3 years

3 - 8 Lacs

chennai porur

Posted:1 day ago| Platform:

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Skills Required

pandas python-based ai development lightgbm scikit-learn numpy ml development pytorch dl libraries xgboost keras catboost ml tensorflow

Work Mode

Work from Office

Job Type

Full Time

Job Description

We are seeking a highly skilled AI/ML Developer to join our team in Chennai. The ideal candidate will have expertise in Python, machine learning libraries, and deep learning frameworks, along with hands-on experience in offline/self-hosted LLMs. You will work on building customized models, integrating pre-trained/self-hosted models, and deploying them at scale. The role involves diverse projects in computer vision, NLP, recommendation systems, MLOps, and data pipelines.
Key Responsibilities
Design, train, and deploy custom ML/DL models using Python.
Work with offline/self-hosted LLMs (LLaMA, Mistral, Falcon, GPT-J, etc.) using
Hugging Face, vLLM, Ollama, LangChain, RAG pipelines.
Develop and optimize models with classical ML libraries:
o Scikit-learn, XGBoost, LightGBM, CatBoost
Build deep learning solutions using:
o PyTorch, TensorFlow, Keras
Work on computer vision tasks with OpenCV, PyTorch/TensorFlow (classification, detection, OCR, segmentation).
Develop NLP applications (text classification, embeddings, summarization, chatbots) using NLTK, SpaCy, Hugging Face Transformers.
Construct and maintain data pipelines using Airflow, Spark, Kafka, or Prefect.
Apply MLOps practices: model versioning, CI/CD, monitoring, retraining workflows.
Deploy models across cloud and on-prem environments (AWS, GCP, Azure, bare-metal).
Collaborate with cross-functional teams to deliver AI-driven solutions.
Required Skills & Experience
Bachelors/Masters in Computer Science, Data Science, AI/ML, or related field.
2 - 3 years of hands-on experience in Python-based AI/ML development.
Strong expertise with ML/DL libraries: Scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch, TensorFlow, Keras.
Proficiency in data analysis and processing with Pandas, NumPy, Dask.
Knowledge of offline/self-hosted LLMs and private deployment stacks.
Familiarity with vector databases (FAISS, Pinecone, Weaviate, Milvus) for RAG.
Experience with MLOps tools (MLflow, Kubeflow, Docker, Kubernetes, SageMaker).
Strong debugging and optimization skills for ML pipelines and models.Nice to Have
Experience with Generative AI (LLMs, diffusion models, fine-tuning).
Exposure to reinforcement learning and advanced recommender systems.
Open-source contributions in Python/AI/ML.

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