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, software engineering, data science, predictive analytics, Python development, model deployment, automation, and AI-powered application development.

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-driven applications, predictive systems, data pipelines, APIs, automation solutions, analytics platforms, and intelligent enterprise applications.

The role may involve working with Python, machine learning libraries, deep learning frameworks, REST APIs, SQL and NoSQL databases, cloud platforms, MLOps tools, data-processing technologies, generative AI, and modern software-engineering practices 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, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Statistics, Electronics, 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, predictive analytics, model development, model deployment, backend 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, AI/ML Engineer, Applied Machine Learning Engineer, Data Scientist, Software Engineer - Machine Learning, Python Software Engineer, Python Developer, Data Engineer,



Associate Software Engineer, Graduate Software Engineer, or Junior 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, object-oriented programming, data structures, algorithms, debugging, and software-development fundamentals.
- Understanding of supervised learning, unsupervised learning, classification, regression, clustering, feature engineering, feature selection, and model-training concepts.
- Familiarity with NumPy, Pandas, scikit-learn, SciPy, or similar Python data and machine learning libraries.
- Exposure to TensorFlow, PyTorch, Keras, XGBoost, LightGBM, CatBoost, or similar machine learning and deep learning frameworks will be beneficial.
- Understanding of data preprocessing, data cleaning, exploratory data analysis, feature transformation, missing-value handling, outlier detection, normalization, scaling, and encoding.
- Basic understanding of statistics, probability, distributions, correlation, sampling, hypothesis testing, and statistical modelling will be advantageous.
- Familiarity with model evaluation techniques including accuracy, precision, recall, F1-score, ROC-AUC, RMSE, MAE, cross-validation, confusion matrices, and model-selection concepts.
- Exposure to deep learning, neural networks, natural language processing, computer vision, recommendation systems, time-series forecasting, or generative AI will be beneficial but is not mandatory.
- Familiarity with SQL, MySQL, PostgreSQL, SQL Server, Oracle, MongoDB, Redis, or similar databases will be advantageous.
- Understanding of REST APIs, JSON, HTTP/HTTPS, backend integration, model-serving APIs, and application integration.
- Exposure to FastAPI, Flask, Django, or similar Python frameworks for deploying and integrating machine learning models will be beneficial.
- Familiarity with Git, GitHub, version control, branching, pull requests, code reviews, and collaborative development workflows.
- Exposure to AWS, Microsoft Azure, Google Cloud, Docker, Kubernetes, CI/CD, Jenkins, GitHub Actions, or similar cloud and DevOps technologies will be considered an advantage.




- Familiarity with MLflow, Kubeflow, Airflow, DVC, Weights & Biases, SageMaker, Azure Machine Learning, Vertex AI, or similar MLOps tools will be beneficial but is not mandatory.
- Exposure to model deployment, inference pipelines, experiment tracking, model versioning, reproducibility, model monitoring, and production machine learning workflows will be advantageous.
- Familiarity with ETL/ELT, data pipelines, Spark, PySpark, Kafka, Hadoop, Databricks, dbt, or related data-engineering technologies will be beneficial.
- Exposure to large language models, transformers, Hugging Face, embeddings, vector databases, retrieval-augmented generation, prompt engineering, LangChain, LlamaIndex, or generative AI technologies will be considered an additional advantage.
- Exposure to OpenCV, spaCy, NLTK, transformers, or other NLP and computer-vision libraries will be beneficial.
- Basic understanding of cloud-based AI services, serverless computing, containerized applications, scalable inference, and distributed computing will be advantageous.
- Exposure to model drift, data drift, explainable AI, responsible AI, data privacy, AI security, or model-performance monitoring will be beneficial.
- Familiarity with Excel, Power BI, Tableau, Looker, dashboards, reporting tools, or business analytics 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 requirements and translate them into practical machine learning, artificial intelligence, analytics, 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, generative AI project, Kaggle project, GitHub 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, cloud platforms, MLOps practices, data tools, and modern software-engineering techniques.

Job Location: Morena, Madhya Pradesh Employment Type: Full-time, On-site

Experience: Freshers & 0–5 Years

📌 Machine Learning Software Engineer (Morena)
🏢 Saatvik Agro
📍 Morena