Transformers, LangChain, Vector Databases, MLOps, Model Monitoring, MACHINE LEARNING, PYTHON, NLP, PYTORCH

Key Responsibilities

- Build, train, evaluate, and iterate Machine Learning models using Python for structured and unstructured data use cases.
- Develop and optimize GenAI solutions (prompting, evaluation, and tuning approaches) aligned to business needs and responsible AI practices.
- Implement NLP pipelines for text preprocessing, feature extraction/embeddings, classification, summarization, or information retrieval tasks.
- Perform data exploration, cleaning, and transformation to ensure high-quality inputs for ML/GenAI workflows.
- Define evaluation metrics, run experiments, analyze results, and communicate insights to technical and non-technical stakeholders.
- Collaborate with cross-functional teams to translate requirements into technical designs and deliverables.
- Support deployment readiness by packaging models, documenting workflows, and assisting integration with downstream systems.
- Monitor model performance and data drift, and contribute to continuous improvement through retraining and refinements.

Minimum

Qualifications:

- Education: BTECH, MTECH, MCA, MSC (or equivalent).
- 3–5 years of experience applying Machine Learning using Python in real-world projects.




- Robust proficiency in Python for data processing, modeling, and experimentation.
- Hands-on experience with ML concepts (supervised/unsupervised learning, feature engineering, model validation).
- Working knowledge of Generative AI concepts and practical implementation approaches.
- Exposure to NLP techniques and text-based modeling workflows.
- Ability to communicate clearly, collaborate effectively, and document solutions for reuse and maintainability.

Preferred

Qualifications:

- Experience building end-to-end NLP solutions (tokenization, embeddings, vector search, evaluation) for production or near-production use cases.
- Familiarity with modern GenAI patterns such as retrieval-augmented generation (RAG), prompt engineering, and response quality evaluation.
- Experience with ML/GenAI experimentation frameworks, reproducibility practices, and model governance basics.
- Strong understanding of model performance tuning, error analysis, and iterative improvement cycles.
- Ability to work with stakeholders to refine problem statements, define success metrics, and deliver measurable outcomes.
- Prior experience contributing to scalable, maintainable analytics/ML codebases with good engineering practic

📌 Python ML /GenAI (Bengaluru)
🏢 Infosys
📍 Bengaluru