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We are looking for a Machine Learning Engineer with 2–4 years of experience in building and deploying ML-driven solutions, including ML and GenAI-based systems. The role involves working across the full ML lifecycle, from data preparation and model development to deployment, monitoring, and continuous improvement in production environments.
Job Responsibility:
Build, train, and optimize machine learning and GenAI models for production use cases
Work with large-scale structured and unstructured datasets (text, images, embeddings, tabular data)
Implement feature engineering, data preprocessing, and model evaluation pipelines
Develop and deploy ML/GenAI models. Integrate ML solutions with backend systems and applications
Work with embedding models, vector databases, or retrieval-based ML systems where applicable
Monitor model performance, latency, and quality in production
Collaborate closely with data engineers, backend engineers, and product teams
Document model behavior, assumptions, and deployment workflows.
Requirements:
2+ years of hands-on experience as a Machine Learning Engineer
Solid understanding of machine learning concepts and algorithms
Experience with frameworks such as PyTorch, TensorFlow, scikit-learn, Langchain, LangGraph etc.
Familiarity with NLP, embeddings, or deep learning–based models
Hands-on experience with GenAI systems (LLMs, prompt engineering, RAG pipelines, fine-tuning)
Experience with data processing using Pandas, NumPy, and SQL
Understanding of model deployment, inference, and performance optimization
Experience using Git and working in Linux-based environments
Experience with cloud platforms such as AWS / GCP / Azure
Experience with Docker and containerized deployments
Strong communication skills with the ability to explain ML/GenAI concepts clearly to both technical and non-technical stakeholders.