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Tier-1 brand, popular ML title, and metro hiring amplify applicant competition.
ML/NLP and RAG specialization narrows fit somewhat, though Python and data engineering skills remain transferable.
Explicit 1–3 years plus required ML/NLP, Python, cloud, and data engineering skills increase filter strictness.
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Develop, fine-tune, and deploy machine learning and NLP models including Transformers, BERT-based models, and Retrieval-Augmented Generation (RAG) pipelines using modern frameworks such as TensorFlow, PyTorch, LangChain, and cloud ML environments.
Design and implement scalable data engineering solutions including ETL pipelines and data storage with SQL/NoSQL databases, big-data frameworks like Spark and Hadoop, ensuring data quality and governance.
Build and maintain production-grade Python backend services (using FastAPI, Flask, Django) to integrate ML solutions, automate workflows, and deliver end-to-end ML and data solutions independently.
Bachelor’s degree in Computer Science, Engineering, or related field.
1-3 years of professional experience working with Machine Learning, NLP, and Data Engineering.
Expert-level proficiency in Python programming with backend/API development experience.
Proficiency with ML/NLP algorithms, SQL and NoSQL databases, big data tools (Spark, Hadoop), and cloud platforms (AWS, Azure).
Experienced in independently owning end-to-end ML and data engineering projects with demonstrated ability to translate business requirements into technical solutions.
Strong technical depth in advanced NLP techniques (Transformers, BERT, LLM fine-tuning) and RAG pipeline development using state-of-the-art frameworks.
Proven capability to design scalable data architectures and develop clean, modular, production-quality Python backend services in collaborative, cross-functional teams.