Machine Learning Software Engineer

Saatvik Agro
Morena, Madhya Pradesh, India

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, model development, backend systems, APIs, cloud technologies, MLOps, automation, and modern AI-driven applications.

The Machine Learning Software Engineer will work closely with software, IT, data, analytics, operations, production, supply chain, finance, sales, and other business teams to build, test, deploy, integrate, maintain, and improve machine-learning models, data-driven applications, APIs, prediction services, automation solutions, analytics systems, and intelligent enterprise tools.

The role may involve working with Python, machine-learning frameworks, data-processing libraries, SQL, REST APIs, model-serving frameworks, cloud platforms, Docker, MLOps tools, data pipelines, version control, testing, monitoring, and modern AI/ML technologies 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, AI development, data engineering, 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, Data Scientist, Applied ML Engineer, Python Software Engineer, ML Software Developer, Software Engineer - Machine Learning, Associate Machine Learning 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, object-oriented programming, data structures, algorithms, functions, classes, exception handling, debugging, and clean coding practices.
  • Understanding of machine-learning fundamentals including supervised learning, unsupervised learning, classification, regression, clustering, feature engineering, model selection, and model evaluation.
  • Familiarity with NumPy, Pandas, scikit-learn, SciPy, or similar Python libraries will be beneficial.
  • Exposure to TensorFlow, PyTorch, Keras, XGBoost, LightGBM, CatBoost, or similar machine-learning and deep-learning frameworks will be advantageous.
  • Understanding of data preprocessing, exploratory data analysis, missing-value handling, encoding, scaling, feature selection, outlier handling, and statistical analysis.
  • Familiarity with model-evaluation concepts such as accuracy, precision, recall, F1-score, ROC-AUC, confusion matrices, RMSE, MAE, cross-validation, and overfitting control will be beneficial.
  • Basic to good understanding of SQL, relational databases, joins, aggregations, subqueries, data extraction, and database querying.
  • Familiarity with MySQL, PostgreSQL, SQL Server, Oracle, MongoDB, or similar database technologies will be advantageous.
  • Exposure to REST APIs, JSON, HTTP/HTTPS, FastAPI, Flask, Django, or similar technologies for model deployment and backend integration will be beneficial.
  • Exposure to model serving, inference pipelines, batch prediction, real-time prediction, API-based ML services, or production ML systems will be considered an advantage.
  • Familiarity with Git, GitHub, version control, branching, pull requests, code reviews, documentation, and collaborative software-development workflows.
  • Exposure to AWS, Microsoft Azure, Google Cloud, SageMaker, Azure Machine Learning, Vertex AI, or similar cloud and machine-learning platforms will be beneficial.
  • Familiarity with Docker, Kubernetes, CI/CD, Jenkins, GitHub Actions, Linux, command-line tools, or cloud deployment concepts will be advantageous.
  • Exposure to MLOps tools and practices such as MLflow, Kubeflow, Airflow, DVC, Weights & Biases, model registries, experiment tracking, automated pipelines, and model monitoring will be beneficial but is not mandatory.
  • Exposure to ETL/ELT, Apache Spark, PySpark, Kafka, Databricks, dbt, Airflow, data pipelines, batch processing, or streaming systems will be considered an advantage.
  • Familiarity with generative AI, large language models, embeddings, vector databases, retrieval-augmented generation, prompt engineering, Hugging Face, LangChain, or LlamaIndex will be beneficial but is not mandatory.
  • Exposure to natural language processing, computer vision, recommendation systems, forecasting, anomaly detection, optimization, or deep-learning applications will be considered an additional advantage.
  • Familiarity with OpenCV, spaCy, NLTK, Transformers, or similar AI/ML libraries will be beneficial.
  • Understanding of model drift, data drift, monitoring, retraining, reproducibility, responsible AI, bias, explainability, and model-performance management will be advantageous.
  • Exposure to analytics, dashboards, Power BI, Tableau, Excel, ERP, CRM, manufacturing systems, supply-chain applications, or enterprise platforms will be considered an additional advantage.
  • Familiarity with Agile, Scrum, Jira, SDLC, technical documentation, requirements gathering, experimentation, or project collaboration will be beneficial 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 and technical requirements and translate them into practical, reliable, scalable, maintainable, and measurable machine-learning solutions.
  • Willingness to work in an on-site environment.
  • Internship, academic project, machine-learning project, AI project, Python project, data-science project, deep-learning project, Kaggle project, GitHub project, hackathon, research project, 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 tools, databases, deployment practices, and modern software-engineering techniques.

Job Location: Morena, Madhya Pradesh

Employment Type: Full-time, On-site

Experience: Freshers & 0–5 Years

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