Technical Lead

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.