Machine Learning Software 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 Software 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 machine learning, artificial intelligence, Python development, software engineering, data science, model development, APIs, automation, cloud technologies, and production AI applications.
The Machine Learning Software Engineer will work closely with software, data, analytics, IT, operations, finance, production, supply chain, sales, and other business teams to develop, test, integrate, deploy, and improve machine-learning models, data-driven applications, automation solutions, and intelligent software systems.
The role may involve working with Python, machine-learning libraries, data-processing frameworks, REST APIs, SQL and NoSQL databases, cloud platforms, model deployment tools, MLOps technologies, analytics systems, and enterprise applications depending on project and business requirements.
Qualifications
B.E. / B.Tech / B.Sc. / BCA / MCA / M.Sc. / M.Tech in Computer Science, Information Technology, Software Engineering, Computer Applications, Data Science, Artificial Intelligence, Machine Learning, 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, artificial intelligence, software engineering, data science, Python development, ML engineering, data engineering, analytics, application development, or related technology roles can apply.
Candidates currently working as Machine Learning Software Engineer, Machine Learning Engineer, AI Engineer, Data Scientist, Python Developer, Software Engineer, Data Engineer, ML Developer, Applied AI Engineer, Associate Data Scientist, or Associate Software Engineer are encouraged to apply.
Candidates from IT services, SaaS, product companies, analytics, consulting, e-commerce, fintech, telecom, manufacturing, logistics, FMCG, or other industries are welcome.
Basic to good knowledge of Python programming and software-development fundamentals.
Understanding of data structures, algorithms, object-oriented programming, debugging, and clean coding practices.
Basic to good understanding of machine-learning concepts such as supervised learning, unsupervised learning, classification, regression, clustering, feature engineering, model evaluation, and overfitting.
Familiarity with Python libraries such as NumPy, Pandas, Scikit-learn, SciPy, Matplotlib, or similar technologies will be beneficial.
Exposure to TensorFlow, PyTorch, Keras, XGBoost, LightGBM, CatBoost, or similar machine-learning frameworks will be an advantage.
Familiarity with data preprocessing, data cleaning, feature selection, transformation, normalization, and exploratory data analysis will be beneficial.
Basic understanding of statistics, probability, linear algebra, optimization, or mathematical foundations of machine learning will be advantageous.
Exposure to deep learning, neural networks, natural language processing, computer vision, recommendation systems, time-series forecasting, or generative AI will be considered an additional advantage but is not mandatory.
Familiarity with SQL, MySQL, PostgreSQL, SQL Server, SQLite, MongoDB, or similar databases will be beneficial.
Exposure to REST APIs, JSON, web services, FastAPI, Flask, Django, or integrating machine-learning models into software applications will be advantageous.
Familiarity with model deployment, model serving, API-based inference, batch inference, or production machine-learning workflows will be beneficial.
Exposure to MLOps tools and practices such as MLflow, Kubeflow, DVC, model monitoring, experiment tracking, model versioning, or automated retraining will be considered an advantage but is not mandatory.
Familiarity with Git, GitHub, version control, code reviews, testing, debugging, and collaborative software-development workflows.
Exposure to AWS, Microsoft Azure, Google Cloud, SageMaker, Azure Machine Learning, Vertex AI, or similar cloud AI/ML platforms will be beneficial but is not mandatory.
Exposure to Docker, Kubernetes, CI/CD, Jenkins, GitHub Actions, or DevOps technologies will be considered an advantage.
Basic understanding of data pipelines, ETL/ELT, data engineering, workflow orchestration, or large-scale data processing will be beneficial.
Exposure to Apache Spark, PySpark, Airflow, Kafka, Databricks, Hadoop, or similar data technologies will be considered an additional advantage but is

Work arrangement
No