Technical Lead

: 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.