Python ML /GenAI

Join a collaborative engineering team where you’ll build intelligent, reliable, and scalable ML/GenAI solutions using Python. In this role, you’ll work closely with product, data, and platform teams to take ideas from experimentation to production—turning models into real-world capabilities that improve user experiences and business outcomes. You’ll contribute to designing robust systems, optimizing model performance, and ensuring deployments are secure, observable, and maintainable. If you enjoy solving ambiguous problems, learning fast-moving GenAI techniques, and delivering practical NLP-driven features, this is a great opportunity to grow your impact while working in a supportive, high-ownership culture.

Responsibilities

Key Responsibilities:

Develop and maintain Python-based ML/GenAI components for NLP use cases, from prototyping to production-ready implementations.

Build and fine-tune models using PyTorch, focusing on accuracy, latency, and resource efficiency.

Design data preprocessing and feature engineering workflows for text datasets, ensuring quality and reproducibility.

Implement evaluation strategies for NLP/GenAI outputs, track metrics, and iterate based on measurable results.

Collaborate with cross-functional teams to translate requirements into model/system designs and deliverables.

Troubleshoot model and pipeline issues, perform root-cause analysis, and improve system reliability.

Write clean, maintainable code with appropriate documentation, tests, and version control practices.

Technical requirements

Primary skills:Technology->AI-Data science->Machine Learning,Technology->AI-Generative AI->Generative AI for Data Analytics,Technology->OpenSystem->Python - OpenSystem->Python

Additional responsibilities

Minimum Qualifications:

Bachelor’s/Master’s degree (or equivalent) in Engineering/Computer Science or related field (BE/BTech/MSc/MCA/MTech).

2–3 years of hands-on experience building ML solutions using Python.

Practical experience with NLP concepts and workflows (tokenization, embeddings, text classification, sequence modeling).

Working knowledge of PyTorch for training, fine-tuning, and inference.

Strong problem-solving skills and ability to collaborate effectively with engineering and data stakeholders.

Preferred Qualifications:

Experience with transformer-based NLP/GenAI approaches (fine-tuning, prompt-based workflows, evaluation techniques).

Exposure to MLOps practices such as experiment tracking, model versioning, and reproducible training pipelines.

Familiarity with deploying ML models as services and optimizing inference performance.

Experience working with modern data/ML tooling for dataset handling, training acceleration, and scalable experimentation.

Demonstrated ability to communicate trade-offs and results clearly to technical and non-technical stakeholders.

Good to have skills:

Hugging Face Transformers, LangChain, Vector Databases (FAISS/Pinecone), MLflow, Docker

Education

MCA,MSc,MTech,Bachelor of Engineering,BTech