Role : AI/ML Engineer

Location : Noida

Experience : 0 to 3 Years

Education : B.Tech / M.Tech (CS / IT / AI / Data Science)

Reports to : AI/ML Lead

Function : Applied Machine Learning AI Engineering Data Science Model Development MLOps Foundations

Job Purpose :

Design, build, train, and deploy machine learning models and AI-driven components for enterprise products across BPM, ECM, CCM, and cloud-native platforms. The role focuses on developing, evaluating, and operationalizing ML/AI models, including classical ML and deep learningnot software testing.

Key Responsibilities :

Model Development & Research :

- Develop, experiment with, and evaluate ML models using structured and unstructured data.

- Perform data preprocessing, feature engineering, exploratory analysis, and model selection.

- Apply supervised and unsupervised learning techniques across classification, prediction, clustering, NLP, and basic CV tasks.

Model Operationalization :

- Optimize and tune models for accuracy, scalability, performance, and robustness.

- Deploy ML models on AWS/Azure using containers, serverless, or managed ML services.

- Expose model inference via REST APIs for integration with enterprise applications.

Data & Engineering Collaboration :

- Translate product/business problems into data-driven ML solutions.

- Work with engineering teams to integrate models into applications and workflows.

- Collaborate with product teams to understand use-cases, constraints, and success metrics.

Monitoring & Improvement :

- Monitor model performance, detect drift, manage retraining cycles, and enhance data quality.

- Document experiments, model behaviour, and reproducibility artifacts.

Key Skills :

- Programming : Python (mandatory)

- ML & Statistics : Supervised/unsupervised learning, model evaluation, statistics

- Frameworks : scikit-learn / TensorFlow / PyTorch (any)

- Data : SQL, basic data engineering

- Cloud : AWS or Azure (basic)

- Engineering : Git, REST APIs, basic CI/CD

Preferred (Good to Have) :

- Exposure to NLP, transformers, classical CV, explainability tools, MLflow, MLOps basics