Roles and Responsibilities :

- - Lead the design, development, training, and deployment of ML, Deep Learning, and Generative AI models, with a strong foundation in Data Science/Machine Learning.
- Develop solutions across prediction, classification, recommendation, forecasting, optimization, NLP, and GenAI use cases beyond traditional LLM applications.
- Perform model fine-tuning and optimization using techniques such as LoRA, PEFT, RLHF, DPO, prompt optimization, quantization, and model distillation.
- Build RAG and Agentic AI solutions, including vector stores, retrievers, and frameworks such as LangGraph, AutoGen, CrewAI, or OpenAI Agents.
- Build and manage end-to-end MLOps pipelines covering experimentation, CI/CD, model deployment, monitoring, evaluation, and lifecycle management using platforms such as MLflow, SageMaker, Databricks, or Kubeflow.
- Design scalable AI solutions on AWS, with basic architecture understanding and hands-on experience deploying production-grade AI/ML systems.




- Work with structured/unstructured data using Python, Pandas, NumPy, Scikit-Learn, PyTorch, TensorFlow, and XGBoost, while collaborating with business stakeholders to deliver measurable outcomes.

Job Requirements :

- 7-14 years of experience in ML Engineering or a related field.
- Robust expertise in deep learning, machine learning engineering, and generative AI.
- Proficiency in Python programming language with experience working on AWS SageMaker

Mandatory Skills:

- Generative AI, LLMs, RAG, Vector Stores & Retrievers
- AWS and basic architecture knowledge
- Python, PyTorch, TensorFlow, Scikit-Learn
- Model fine-tuning (LoRA, PEFT, RLHF, DPO)
- MLOps tools such as SageMaker, MLflow, Kubeflow, Databricks
- Agentic AI frameworks (LangGraph, AutoGen, CrewAI, OpenAI Agents)
- Machine Learning, Deep Learning, NLP, and AI model deployment experience.

📌 LLM Engineer (India)
🏢 e-Solutions
📍 India