Technical Lead

Lumina Datamatics Inc.; Madurai District, Chennai District, Puducherry; Hybrid; INR 1,800,000 - 2,800,000; Full time

Job summary

Lumina Datamatics Inc. is seeking a skilled AI/ML Engineer to own end‑to‑end ML lifecycle tasks, from data curation to production deployment. You will train, fine‑tune, and benchmark models, build robust inference pipelines, and ensure scalable, reliable AI solutions across projects.

The role requires strong Python expertise, ML library proficiency, and experience with deployment, monitoring, and cloud/MLOps practices.

Qualifications

• Hands-on experience across the end-to-end ML lifecycle.
• Proficiency in Python and ML libraries (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow).
• Experience deploying AI/ML models in production with scalable pipelines.

Responsibilities

• Curate and prepare training, validation, and test datasets.
• Train, fine-tune, and benchmark ML models; optimize for performance.
• Deploy models via APIs and inference pipelines; ensure monitoring and impact.
• Troubleshoot training/inference issues; optimize for latency and cost.
• Collaborate with cross-functional teams to productionize AI solutions.

Skills

Python

Dataset preparation

Model training

Fine-tuning

Deployment

REST APIs

Docker

CI/CD

Cloud platforms

MLOps

LLMs

Tools

PyTorch

TensorFlow

NumPy

Pandas

Scikit-learn

Job description

Job Description: AI/ML Engineer

Job Title

Tech Lead - AI

Job Summary

We are seeking a skilled AI/ML Engineer with strong hands-on experience across the end-to-end machine learning lifecycle, from dataset curation and model development to evaluation, deployment, and production optimization. The ideal candidate should have practical expertise in training and fine-tuning machine learning and deep learning models, benchmarking model performance, deploying AI/ML solutions, and troubleshooting complex technical issues to ensure reliable, scalable, and efficient systems.

Key Responsibilities

1. Dataset Curation and Preparation

• Collect, clean, preprocess, and curate datasets for AI/ML model development.
• Perform data validation, transformation, annotation, labeling, and quality checks.
• Identify and address missing values, data inconsistencies, class imbalance, and potential data leakage.
• Prepare high-quality training, validation, and test datasets.

2. Model Training and Fine-Tuning

• Develop, train, and fine-tune machine learning and deep learning models based on project requirements.
• Apply appropriate algorithms, architectures, hyperparameter tuning, and optimization techniques.
• Work with pre-trained models and adapt them to domain-specific use cases.
• Optimize training workflows for performance, accuracy, and computational efficiency.

3. Model Evaluation and Benchmarking

• Evaluate model performance using relevant metrics and validation methodologies.
• Conduct benchmarking against baseline models and established performance standards.
• Perform error analysis, identify performance gaps, and implement improvements.
• Assess model robustness, generalization, reliability, and suitability for real-world applications.

4. Model Deployment

• Deploy trained models into development, testing, and production environments.
• Develop and integrate model-serving APIs and inference pipelines.
• Work with containerization, CI/CD pipelines, cloud platforms, and model deployment frameworks.
• Implement model versioning, monitoring, logging, and performance tracking.

5. Troubleshooting and Optimization

• Diagnose and resolve issues related to model accuracy, training stability, inference latency, and resource utilization.
• Optimize AI/ML systems for scalability, reliability, throughput, and cost efficiency.
• Monitor deployed models for performance degradation and data or concept drift.
• Collaborate with engineering and data teams to resolve production issues and continuously improve system performance.

Required Skills and Qualifications

• Strong practical experience in dataset preparation, model training, fine-tuning, evaluation, deployment, and optimization.
• Proficiency in Python and relevant data science and machine learning libraries, such as NumPy, Pandas, Scikit-learn, PyTorch, or TensorFlow.
• Experience with data preprocessing, feature engineering, model validation, and performance benchmarking.
• Hands-on experience deploying and maintaining AI/ML models in production environments.
• Knowledge of REST APIs, Git, Docker, and CI/CD practices.
• Understanding of cloud-based AI/ML infrastructure and model monitoring.
• Strong analytical, debugging, and problem-solving skills.
• Ability to work collaboratively with cross-functional teams and deliver production-ready AI/ML solutions.

Preferred Qualifications

• Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
• Familiarity with MLOps tools and practices, including experiment tracking, model registries, and automated deployment pipelines.
• Experience with large language models (LLMs), generative AI, or retrieval-augmented generation (RAG), where relevant to the project.
• Knowledge of model quantization, pruning, distributed training, or inference optimization.
• Experience working with large-scale datasets and production AI/ML systems.

Experience

Candidates should have Min 8-12 years of hands-on experience in building, training, evaluating, deploying, and maintaining AI/ML solutions. The required years of experience can be defined based on the seniority of the role.

Key Success Criteria

• High-quality and reliable dataset preparation.
• Improved model accuracy and measurable benchmark performance.
• Successful deployment of scalable, production-ready models.
• Reduced inference latency and computational overhead.
• Effective troubleshooting and continuous optimization of AI/ML systems.