Python ML /GenAI

Infosys; Bengaluru; On-site; INR 2,500,000 - 3,800,000; Full time

Job summary

Infosys in Bengaluru seeks a Machine Learning Engineer to build, train, and optimize GenAI and NLP pipelines using Python.

The role focuses on end-to-end ML workflows, model evaluation, and collaboration with cross-functional teams to deliver production-ready solutions while ensuring responsible AI practices.

Experience with LangChain and vector databases is preferred, with hands-on exposure to MLOps and model monitoring for reliable deployments.

Qualifications

• 3–5 years of ML experience with Python in real projects.
• Strong Python proficiency for data processing and modelling.
• Knowledge of Generative AI concepts and practical implementation.
• Experience with NLP techniques and text modeling workflows.
• Excellent communication and collaboration skills.

Responsibilities

• Build, train, evaluate, and iterate ML 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.

Skills

Python

Machine Learning

GenAI concepts

NLP techniques

Collaboration

Education

BTech/MTech/MCA/MSc

Tools

LangChain

Vector Databases

MLOps

PyTorch

Job description

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.
• Strong 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