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 technology professionals interested in machine learning, artificial intelligence, data science, predictive analytics, deep learning, generative AI, model development, data processing, APIs, cloud technologies, and modern ML engineering practices.
The Machine Learning Engineer will work closely with software, IT, data, analytics, operations, production, supply chain, sales, finance, and other business teams to develop, test, deploy, monitor, maintain, and improve machine learning models, data-driven applications, predictive systems, automation solutions, intelligent workflows, analytics platforms, and enterprise AI solutions.
The role may involve working with Python, NumPy, Pandas, scikit-learn, TensorFlow, PyTorch, SQL, machine learning algorithms, deep learning, NLP, computer vision, generative AI, APIs, cloud platforms, MLOps tools, data pipelines, and modern AI 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, 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, data science, predictive modelling, ML engineering, software engineering, data analytics, or related technology roles can apply.
Candidates currently working as Machine Learning Engineer, ML Engineer, AI Engineer, Artificial Intelligence Engineer, Data Scientist, Applied ML Engineer, Machine Learning Developer, AI/ML Engineer, Python Developer, Data Analyst, Software Engineer, Associate Machine Learning Engineer, Graduate Engineer, or Junior Machine Learning 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, functions, modules, debugging, and clean coding practices.
Familiarity with NumPy, Pandas, scikit-learn, SciPy, or similar Python data and machine-learning libraries will be beneficial.
Understanding of supervised learning, unsupervised learning, regression, classification, clustering, feature engineering, model selection, and model evaluation.
Basic knowledge of probability, statistics, linear algebra, data interpretation, hypothesis testing, and analytical concepts will be advantageous.
Familiarity with model evaluation metrics such as accuracy, precision, recall, F1-score, ROC-AUC, MAE, MSE, RMSE, or similar metrics will be beneficial.
Exposure to TensorFlow, PyTorch, Keras, XGBoost, LightGBM, CatBoost, or similar machine-learning and deep-learning frameworks will be advantageous.
Familiarity with data preprocessing, data cleaning, missing-value treatment, feature scaling, encoding, exploratory data analysis, and data visualization.
Exposure to deep learning, neural networks, natural language processing, computer vision, recommendation systems, forecasting, time-series analysis, or anomaly detection will be beneficial but is not mandatory.
Familiarity with generative AI, large language models, prompt engineering, embeddings, vector databases, retrieval-augmented generation, or similar technologies will be considered an advantage.
Exposure to Hugging Face, LangChain, LlamaIndex, OpenAI-compatible APIs, or similar AI-development frameworks will be beneficial but is not mandatory.
Basic understanding of SQL, MySQL, PostgreSQL, SQL Server, MongoDB, or similar databases and data-querying concepts.
Familiarity with REST APIs, JSON, FastAPI, Flask, Django, or similar technologies for serving and integrating machine-learning models will be advantageous.
Exposure to model deployment, inference pipelines, batch prediction, real-time prediction, model APIs, and production ML systems 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, SageMaker, Azure Machine Learning, Vertex AI, or similar cloud AI and ML platforms will be considered an advantage.
Exposure to Docker, Kubernetes, CI/CD, Jenkins, GitHub Actions, or similar deployment and DevOps technologies will be beneficial.
Familiarity with MLOps tools and concepts such as MLflow, Kubeflow, Airflow, DVC, Weights & Biases, model versioning, experiment tracking, model monitoring, and reproducible pipelines will be advantageous.
Exposure to ETL/ELT, data pipelines, Spark, PySpark, Kafka, Databricks, dbt, or similar data-engineering technologies will be beneficial but is not mandatory.
Familiarity with OpenCV, spaCy, NLTK, Transformers, or similar computer vision and NLP libraries will be considered an additional advantage.
Basic understanding of model drift, data drift, model monitoring, bias, explainability, responsible AI, privacy, security, and ethical AI concepts will be beneficial.
Exposure to Power BI, Tableau, Excel, dashboards, reporting tools, or business analytics will be considered an advantage.
Exposure to automation, ERP, CRM, manufacturing analytics, demand forecasting, quality analytics, predictive maintenance, supply-chain analytics, or enterprise AI applications will be beneficial.
Familiarity with Agile, Scrum, Jira, SDLC, technical documentation, requirements gathering, experimentation, 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 and technical requirements and translate them into practical, measurable, scalable, maintainable, and reliable machine-learning solutions.
Willingness to work in an on-site environment.
Internship, academic project, machine-learning project, AI project, data-science project, Python 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 algorithms, AI frameworks, cloud platforms, MLOps tools, data technologies, and modern AI-engineering practices.
Job Location: Morena, Madhya Pradesh
Employment Type: Full-time, On-site
Experience: Freshers & 0–5 Years

Work arrangement
No