Machine Learning Engineer

Company Description
Saatvik Agro is the agro-ingredient unit of the Saatvik Group, specializing in high-quality maize-based ingredients used in food, nutrition, animal feed, and industrial applications. The organization focuses on purity and scientific rigor, converting responsibly sourced maize into functional and reliable ingredient solutions. Its products are designed to meet the evolving needs of modern manufacturers who demand consistency, performance, and safety. Guided by the belief that better ingredients create better outcomes, Saatvik Agro aims to support customers in delivering superior products to their markets.
Role Description
We are looking for a Machine Learning Engineer for a full-time, on-site opportunity based in Morena, Madhya Pradesh, India.
The role is suitable for fresh graduates and early-career technology professionals interested in working across Machine Learning, Artificial Intelligence, Python, Data Science, Predictive Analytics, Model Deployment, MLOps, Cloud, and Software Engineering.
The Machine Learning Engineer will work closely with software, data, IT, analytics, operations, production, supply chain, finance, sales, and other business teams to develop, deploy, monitor, and improve machine-learning models and AI-driven solutions.
Qualifications
B.E. / B.Tech / B.Sc. / BCA / MCA / M.Sc. / M.Tech in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Information Technology, Software Engineering, Mathematics, Statistics, or a related discipline.
Freshers and experienced candidates are strongly encouraged to apply.
Candidates with 0–5 years of experience in machine learning, AI, data science, Python development, software engineering, MLOps, data engineering, or related technology roles can apply.
Candidates currently working as Machine Learning Engineer, AI Engineer, Data Scientist, Applied ML Engineer, Python Developer, Software Engineer, MLOps Engineer, Data Engineer, Research Engineer, or Associate Data Scientist are encouraged to apply.
Candidates from IT services, SaaS, product companies, technology, analytics, consulting, fintech, e-commerce, telecom, manufacturing, logistics, or other industries are welcome.
Basic to good knowledge of Python programming.
Understanding of machine learning, statistics, probability, data structures, algorithms, and analytical concepts.
Familiarity with Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM, or similar machine-learning frameworks will be beneficial.
Familiarity with NumPy, Pandas, SciPy, Matplotlib, or similar data and scientific-computing libraries.
Understanding of data preprocessing, feature engineering, model training, validation, evaluation metrics, and hyperparameter tuning.
Basic to good knowledge of SQL and databases such as MySQL, PostgreSQL, SQL Server, MongoDB, or similar platforms.
Exposure to FastAPI, Flask, Django, REST APIs, or backend development will be beneficial.
Exposure to AWS, Azure, Google Cloud, SageMaker, Azure Machine Learning, Vertex AI, Databricks, or similar platforms will be an advantage but is not mandatory.
Familiarity with Docker, Kubernetes, MLflow, Airflow, Kubeflow, DVC, CI/CD, or other MLOps technologies will be beneficial but is not mandatory.
Exposure to NLP, computer vision, deep learning, generative AI, LLMs, forecasting, recommendation systems, or optimization will be considered an advantage but is not mandatory.
Familiarity with Git, GitHub, version control, testing, debugging, and collaborative development workflows.
Basic understanding of model deployment, monitoring, model drift, retraining, and production ML systems will be beneficial.
Good analytical, mathematical, logical, debugging, and problem-solving skills.
Good communication, documentation, and teamwork abilities.
Ability to work effectively with software, data, analytics, and business teams.
Willingness to work in an on-site environment.
Internship, Kaggle project, machine-learning project, GitHub project, hackathon, research project, AI application, freelance project, or academic project will be considered but is not mandatory.
Strong willingness to learn new machine-learning, AI, data, cloud, and MLOps technologies.

Work arrangement
No