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 technology professionals interested in machine learning, artificial intelligence, Python development, software engineering, data science, model development, APIs, data pipelines, cloud technologies, MLOps, and modern AI applications. The Machine Learning Software Engineer will work closely with software, IT, data, analytics, operations, finance, production, supply chain, sales, and other business teams to develop, test, deploy, integrate, maintain, and improve machine-learning models, AI-enabled applications, data-processing workflows, APIs, automation solutions, and digital systems. The role may involve working with Python, scikit-learn, TensorFlow, PyTorch, Pandas, NumPy, SQL, REST APIs, data pipelines, cloud platforms, model deployment technologies, MLOps tools, and modern machine-learning frameworks 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, data science, software engineering, Python development, ML engineering, data engineering, analytics, model development, or related technology roles can apply. - Candidates currently working as Machine Learning Software Engineer, Machine Learning Engineer, ML Engineer, AI Engineer, Artificial Intelligence Engineer, Data Scientist, Junior Data Scientist, Python Developer, Python Software Engineer, Data Engineer, Software Engineer, Applied ML Engineer, Junior Machine Learning Engineer, or Associate Software Engineer are encouraged to apply. - Candidates from IT services, SaaS, product companies, technology, consulting, e-commerce, fintech, telecom, analytics, 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, modular programming, exception handling, and clean coding practices. - Basic understanding of supervised learning, unsupervised learning, classification, regression, clustering, feature engineering, model selection, and machine-learning fundamentals. - Familiarity with NumPy, Pandas, scikit-learn, SciPy, or similar Python libraries will be beneficial. - Exposure to TensorFlow, PyTorch, Keras, XGBoost, LightGBM, or similar machine-learning and deep-learning frameworks will be advantageous. - Basic understanding of data preprocessing, data cleaning, feature extraction, feature scaling, missing-value handling, exploratory data analysis, and dataset preparation. - Understanding of statistics, probability, hypothesis testing, distributions, correlation, evaluation metrics, and basic mathematical concepts used in machine learning will be beneficial. - Familiarity with model evaluation techniques such as train-test split, cross-validation, precision, recall, F1 score, ROC-AUC, RMSE, MAE, confusion matrices, or similar metrics 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. - Basic understanding of SQL, MySQL, PostgreSQL, SQL Server, MongoDB, or similar databases will be beneficial. - Familiarity with REST APIs, JSON, HTTP/HTTPS, FastAPI, Flask, Django, or similar technologies for integrating machine-learning models into software applications will be advantageous. - Exposure to model deployment, inference APIs, batch prediction, real-time prediction, model serving, or production machine-learning systems will be beneficial. - Familiarity with Git, GitHub, version control, branching, pull requests, code reviews, debugging, and collaborative software-development workflows. - Exposure to AWS, Microsoft Azure, Google Cloud, Docker, Kubernetes, serverless technologies, or cloud-based machine-learning services will be considered an advantage. - Exposure to MLOps concepts such as experiment tracking, model versioning, model monitoring, automated training pipelines, deployment pipelines, and model lifecycle management will be advantageous. - Familiarity with tools such as MLflow, Kubeflow, Airflow, DVC, Weights & Biases, SageMaker, Azure Machine Learning, Vertex AI, or similar technologies will be beneficial but is not mandatory. - Exposure to ETL/ELT, data pipelines, Apache Spark, PySpark, Kafka, Databricks, Airflow, dbt, or similar data-engineering technologies will be considered an additional advantage. - Familiarity with Docker, CI/CD, Jenkins, GitHub Actions, Linux, command-line tools, virtual environments, dependency management, and application deployment will be beneficial. - Exposure to large language models, vector databases, embeddings, retrieval-augmented generation, prompt engineering, Hugging Face, LangChain, or similar generative-AI technologies will be considered an additional advantage. - Exposure to OpenCV, spaCy, NLTK, transformers, computer-vision libraries, NLP libraries, or related AI-development technologies will be beneficial. - Basic understanding of model performance, scalability, latency, monitoring, reliability, data drift, model drift, and production troubleshooting will be advantageous. - Familiarity with data privacy, model security, responsible AI, access controls, secure APIs, and safe handling of business data will be beneficial. - Exposure to Power BI, Tableau, Excel, dashboards, reporting systems, or analytics tools will be considered an additional advantage. - Familiarity with Agile, Scrum, Jira, SDLC, technical documentation, requirements gathering, sprint planning, or issue tracking will be advantageous but is not mandatory. - Good analytical, mathematical, logical, debugging, troubleshooting, and problem-solving skills. - Good communication, documentation, collaboration, and teamwork abilities. - Ability to understand business problems and translate them into practical, reliable, scalable, and maintainable machine-learning and software solutions. - Willingness to work in an on-site environment. - Internship, academic project, machine-learning project, AI project, data-science project, Python project, deep-learning project, NLP project, computer-vision project, GitHub project, Kaggle project, hackathon, freelance assignment, startup project, or open-source contribution will be considered but is not mandatory. - Candidates without previous full-time machine-learning experience can also apply. - Strong willingness to learn new machine-learning frameworks, AI technologies, programming tools, cloud platforms, data architectures, and modern MLOps practices. Job Location: Morena, Madhya Pradesh Employment Type: Full-time, On-site Experience: Freshers & 0–5 Years