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Predigle - Product Engineer - Data Science

Predigle
Multiple Locations
1 - 5 Years

Posted on: 25/02/2026

Job Description

Role : Product Engineer (Data Scientist)


Experience : 1 to 5 Years


Work Mode : WFO / Hybrid / Remote if applicable


Immediate Joiners Preferred


Required Skills & Qualification :


The ideal candidate will have experience building AI-powered, data-driven products, working across the full lifecycle - from data analysis and model development to deployment and integration into production systems.


- Strong experience in Python programming.


- Hands-on experience in Data Science, Machine Learning, and NLP.


- Proficiency in PyTorch, Scikit-learn, NLTK, SpaCy, Hugging Face.


- Experience working with LLMs and transformer models (BERT, GPT, etc.).


- Expertise in SQL and data analysis.


- Experience cleaning, preprocessing, and analysing large structured and unstructured datasets.


- Familiarity with Big Data tools such as Pandas, Spark, or Dask.


- Experience integrating ML/NLP models into production systems.


- Exposure to AWS, Azure, or GCP.


- Familiarity with MLOps tools like MLflow, Airflow, or similar.


- Strong analytical, problem-solving, and communication skills.


- Ability to work in a fast-paced, agile, startup environment.


Nice to Have Requirements for the Candidate :


- Exposure to AI or ML-enabled product development.


- Experience with CI/CD, DevOps, and cloud-native deployments.


- Knowledge of agentic AI frameworks or AI automation tools.


- High curiosity, ownership mindset, and learning agility.


- Ability to communicate and collaborate with the team at ease.


- Drive to get the results and not let anything get in your way.


- Critical and analytical thinking skills, with a keen attention to detail.


- Demonstrate ownership and strive for excellence in everything you do.


- Demonstrate a high level of curiosity and keep abreast of the latest technologies & tools


- Ability to pick up new software easily and represent yourself peers and co-ordinate during meetings with Customers.


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