Artificial Intelligence/Machine Learning Engineer - Data Modeling

We are seeking an experienced AI/ML Engineer with a strong foundation in software engineering, machine learning, and production-grade model development. The ideal candidate should have hands-on experience building scalable machine learning systems, writing high-quality code, and working with modern ML frameworks.

Key Responsibilities :

- Research, design, and develop ML models for various use cases such as prediction, classification, clustering, NLP, or recommendation systems.

- Implement end-to-end ML pipelines - from data preprocessing, feature engineering, model training, hyperparameter tuning, and evaluation to deployment.

- Work with frameworks like PyTorch, TensorFlow, Scikit-learn to build and optimize ML models. Conduct model performance evaluation using appropriate metrics (precision, recall, AUC, F1-score, RMSE, etc.).

- Deploy machine learning models into production environments using CI/CD pipelines and scalable infrastructure.

- Build reusable, maintainable, and modular ML components and tooling.

- Collaborate with MLOps teams to define best practices for model versioning, monitoring, retraining, and lifecycle management.

- Optimize model performance, latency, and compute efficiency for large-scale deployments.

- Write clean, efficient, production-ready Python code following best practices.

- Contribute to system architecture and design discussions for ML-driven systems.

- Develop microservices, APIs, or backend integrations to incorporate ML models into products. Work with distributed systems, containerization (Docker, Kubernetes), and cloud platforms (AWS/GCP/Azure).

- Partner with data engineers to understand data pipelines, ensure data quality, and design data processing workflows. Perform exploratory data analysis (EDA) to understand patterns and identify potential ML opportunities.

- Build scalable feature stores, automate data transformations, and maintain training/validation datasets.

- Research, Experimentation & Innovation Stay updated with the latest advancements in machine learning, deep learning, and AI.

- Experiment with new algorithms, architectures, and tools to improve model accuracy and robustness.

- Propose and implement innovative ML approaches to solve business problems.

- Work closely with product teams, domain experts, designers, and engineers to translate business needs into ML solutions.

- Communicate results and insights clearly through reports, dashboards, or visualizations.

- Participate in code reviews, architecture reviews, and team knowledge-sharing sessions.

Required Skills & Experience :

- 5+ years of hands-on experience as an ML Engineer, AI Engineer, or Software Engineer with strong ML focus.

- Proven experience building, training, and deploying ML models in production.

- Expert-level Python programming with clean, modular, well-documented code.

- Experience with additional languages like Java, Go, or C++ is a plus.

- Data structures & algorithms Object-oriented design Design patterns Distributed systems fundamentals

- Machine Learning & Deep Learning Strong theoretical and practical understanding of : Supervised & unsupervised learning Feature engineering Model tuning & optimization Evaluation metrics

- Hands-on experience with PyTorch, Scikit-learn, and/or TensorFlow.

- Knowledge of ML model explainability and interpretability tools is desirable. Version control (Git) Cloud ML platforms (AWS Sagemaker, GCP Vertex AI, Azure ML) Docker/Kubernetes MLflow, DVC, or similar model management tools CI/CD pipelines

- Familiarity with big data frameworks (Spark, Databricks) is an advantage.

- Strong analytical problem-solving abilities. Excellent communication and documentation skills.

- Ability to work independently and collaboratively in cross-functional teams.

- Curious, innovative mindset with the drive to explore and experiment.

- Experience with LLMs, NLP, or deep learning architectures (Transformers, CNNs, RNNs).

- Exposure to reinforcement learning or generative AI.

- Research publications or participation in ML competitions (Kaggle etc.).

- Experience working in high-scale startup or product environments.