AI ML Engineer

Company Description Vamiqa Laboratories Pvt Ltd is a rapidly growing analytical testing and research organization that delivers precise, reliable, and compliance-focused solutions across multiple industries. The company serves food, spices, pharmaceutical, environmental, and industrial sectors through services such as quality analysis, contaminant detection, pesticide residue testing, nutritional profiling, and shelf-life studies aligned with FSSAI, APEDA, and other national and international standards. Equipped with advanced instruments like HPLC, LC-MS, and GC, Vamiqa ensures high accuracy, reproducibility, and strong data integrity. The organization also supports clients with LIMS, QMS, and structured documentation to improve traceability, efficiency, and audit readiness, positioning itself as a strategic quality partner. Vamiqa is expanding into sustainability and environmental monitoring, including GHG analysis and advanced technologies such as NIR spectroscopy and chemometric modeling for faster, smarter, non-destructive testing.

Role Description We are seeking an experienced AI/ML Engineer with hands-on expertise in machine learning, regression modelling, spectral data analysis, and chemometrics. The candidate will work with our team to build robust prediction models using NIR spectral data and laboratory reference measurements. The engineer will also evaluate new algorithms and tools, maintain model performance, and ensure that solutions meet regulatory and compliance expectations. This is a full-time, on-site role based in Hyderabad.

Required Skills

• Strong knowledge of Python, NumPy, Pandas, SciPy, and Scikit-learn.
• Practical experience in regression modelling and predictive analytics.
• Experience with spectral data, signal processing, or chemometrics.
• Knowledge of PLS Regression (PLSR), PCA, SVR, Random Forest, and XGBoost.
• Understanding of data preprocessing, cross-validation, model validation, and overfitting prevention.
• Ability to analyze real-world experimental datasets and improve model performance systematically.

Qualifications

• Strong foundation in Computer Science and Algorithms, with the ability to design efficient, scalable solutions.
• Applied experience in Statistics and Pattern Recognition for building robust analytical and predictive models.
• Hands-on expertise in Neural Networks and modern machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Proficiency in programming languages commonly used in AI/ML such as Python, R, or similar, and experience with data processing tools.
• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related technical field.
• Experience working with scientific, laboratory, or industrial datasets; familiarity with LIMS or QMS environments is an advantage.
• Ability to collaborate with multidisciplinary teams, communicate technical concepts clearly, and document models and workflows thoroughly.
• Strong problem-solving mindset, attention to data quality, and commitment to ethical, responsible use of AI in compliance-driven settings.