Job Description :

We are looking for a skilled AI/ML Engineer with 4+ years of hands-on experience in developing, evaluating, deploying, and monitoring machine learning solutions.

The ideal candidate should have strong expertise in Python, machine learning frameworks, time-series forecasting, feature engineering, data preparation, and model optimization.

The candidate will be responsible for taking ML models from development through production while ensuring performance, reliability, and scalability.

Experience with MLOps, CI/CD, cloud platforms, Generative AI, LLMs, RAG, or Agentic AI will be an added advantage.

Key Responsibilities :

- Design, develop, train, evaluate, and deploy machine learning models for real-world business use cases.

- Develop end-to-end ML pipelines, covering data preparation, feature engineering, model training, evaluation, deployment, and monitoring.

- Perform data preprocessing, cleaning, transformation, feature engineering, and exploratory data analysis.

- Develop and optimize ML models using Python and frameworks such as Scikit-learn, PyTorch, and TensorFlow.

- Develop Time Series Forecasting models using techniques and frameworks such as Prophet and other forecasting approaches.

- Evaluate model performance using appropriate metrics and validation techniques.

- Perform model optimization and hyperparameter tuning to improve accuracy, performance, and scalability.

- Work with structured and unstructured datasets to identify patterns, trends, and insights.

- Build reusable Python-based ML components, pipelines, and applications.

- Deploy ML models into production environments and ensure their reliability and scalability.

- Implement ML model monitoring to track model performance, data quality, drift, and production issues.

- Collaborate with Data Scientists, Data Engineers, Software Engineers, and Product teams to translate business requirements into ML solutions.

- Troubleshoot model and pipeline issues and continuously improve production ML workflows.

- Follow software engineering best practices including version control, testing, documentation, and code reviews.

Required Skills :

- 4 - 10 years of professional experience in AI/ML, Machine Learning Engineering, Data Science, or a related field.

- Strong programming experience in Python.

- Strong understanding of Machine Learning concepts and algorithms.

- Hands-on experience with machine learning frameworks/libraries such as Scikit-learn, PyTorch, and TensorFlow.

- Experience in Time Series Forecasting, preferably using Prophet or similar forecasting frameworks.

- Strong knowledge of feature engineering and data preparation techniques.

- Experience with model development, training, evaluation, deployment, and monitoring.

- Experience with model optimization and hyperparameter tuning.

- Good understanding of statistics, probability, and data analysis.

- Experience working with Pandas and NumPy.

- Good knowledge of SQL and working with structured data/databases.

- Strong analytical and problem-solving skills.

Good to Have:

MLOps & Production Engineering:

- Knowledge of MLOps practices and machine learning lifecycle management.

- Experience with CI/CD pipelines for ML applications.

- Experience with model versioning and experiment tracking.

- Experience deploying and managing ML models in production environments.

- Familiarity with tools such as MLflow or similar MLOps platforms.

- Experience with Docker/Kubernetes is an added advantage.

Cloud & Modern Data Platforms:

- Experience working with cloud platforms such as AWS, Azure, or GCP.

- Exposure to modern cloud-based data platforms and data environments.

- Experience with cloud-based ML services or deployment environments is an advantage.

Generative AI & Emerging Technologies:

- Experience with Generative AI and Large Language Models (LLMs).

- Knowledge of RAG (Retrieval-Augmented Generation) architectures.

- Experience with embeddings and vector databases.

- Understanding of prompt engineering.

- Exposure to Agentic AI / AI Agents and agent-based architectures.

- Experience integrating LLMs into production applications is an added advantage.

Qualifications:

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, Mathematics, Statistics, or a related field.

- Strong communication and collaboration skills.

- Ability to work independently and as part of a cross-functional team.

- Strong interest in learning and working with emerging AI/ML technologies.

What We Offer:

- Opportunity to work on AI/ML and emerging technology initiatives.

- Exposure to real-world Machine Learning, Time Series Forecasting, and AI use cases.

- Opportunity to work with modern cloud, MLOps, and data technologies.

- Exposure to Generative AI, LLMs, RAG, and Agentic AI initiatives.

- Collaborative and learning-focused work environment.

- Career growth opportunities and exposure to innovative projects.