Python AI/ML Engineer

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