Python AI/ML Engineer

Job description Role Overview:- • Join a global IT services leader on an AI/ML engineering engagement building production-grade machine learning systems on AWS infrastructure. • Design, develop, and deploy ML models that move from experimentation to reliable production pipelines serving real business outcomes. • Work in a hybrid setup across Gurgaon, Noida, or Hyderabad, with a team that values engineering rigour alongside data science depth. Key Responsibilities:- • Design and develop ML models for classification, regression, NLP, computer vision, or generative AI use cases depending on project needs. • Build and maintain end-to-end ML pipelines from data ingestion and feature engineering through model training, evaluation, and deployment. • Deploy and manage ML workloads on AWS using services such as SageMaker, Lambda, EC2, S3, and related infrastructure. • Collaborate with data engineers, software engineers, and business stakeholders to translate requirements into production ML systems. • Monitor deployed models for drift, performance degradation, and reliability; own the feedback loop into retraining pipelines. • Write clean, testable Python code and contribute to code reviews; uphold engineering standards across the ML codebase. • Document model design decisions, experiment results, and deployment runbooks. Must-Have:- • 35 years of hands-on experience building and deploying ML models in Python. • Strong proficiency in Python and the ML/data science stack (scikit-learn, PyTorch, TensorFlow, or equivalent). • Hands-on experience with AWS SageMaker, S3, EC2, Lambda, or equivalent ML infrastructure services. • Experience building production ML pipelines (feature stores, training jobs, model serving, monitoring). • Solid understanding of ML fundamentals: model selection, evaluation metrics, overfitting, bias-variance tradeoff. • Familiarity with version control (Git) and collaborative engineering workflows. Good to Have:- • Experience with MLOps tooling (MLflow, Kubeflow, Weights & Biases, or equivalent). • Exposure to large language models (LLMs), fine-tuning, or RAG architectures. • Familiarity with data pipeline tooling (Airflow, Spark, dbt, or equivalent). • Experience with containerisation (Docker, Kubernetes) for ML workloads. • AWS certification (ML Specialty, Solutions Architect, or equivalent).Role & responsibilities