AI/ML ENGINEER

AI/ML ENGINEER

Location: Delhi-NCR

Experience: 4-7 Years

Employment Type: Full-Time

Work Mode: On-Site

CTC: ₹14-18 LPA

Job Summary

We are seeking a skilled and results-driven AI/ML Engineer to develop, train, fine-tune, and optimise machine learning and deep learning models for real-world applications.

The ideal candidate should have 4-7 years of hands-on AI/ML experience, with practical exposure to Natural Language Processing (NLP), Computer Vision, or Predictive Analytics. Strong programming skills in Python, C++, and R are required, along with experience in PyTorch, TensorFlow, Keras, and Hugging Face.

The role involves developing AI/ML models, building data preprocessing and feature engineering pipelines, conducting experiments, evaluating model performance, debugging issues, and collaborating with engineering teams on production deployment. Experience with cloud-based ML platforms, containerisation, model tracking, and version control is essential.

Key Responsibilities

AI/ML Model Development & Optimisation

Develop, train, fine-tune, and optimise machine learning and deep learning models.

Implement AI/ML solutions using open-source frameworks and cloud-based platforms.

Work on NLP, Computer Vision, Predictive Analytics, or related AI/ML applications.

Experiment with model architectures and hyperparameters to improve performance.

Debug model errors and optimise accuracy, latency, reliability, and resource utilisation.

Data Preprocessing & Feature Engineering

Build data preprocessing pipelines for structured and unstructured datasets.

Perform data cleaning, transformation, normalisation, feature extraction, and augmentation.

Work with SQL, NoSQL databases, and relevant data processing tools.

Identify data quality issues and prepare datasets for training, validation, and testing.

NLP, Computer Vision & Predictive Analytics

Develop and fine-tune NLP models using Hugging Face, Transformers, and spaCy.

Work on text classification, information extraction, language modelling, and related NLP applications.

Develop Computer Vision solutions using OpenCV and deep learning frameworks.

Build predictive models for forecasting, classification, regression, and other analytical applications.

Select suitable modelling techniques based on project requirements and dataset characteristics.

Model Evaluation & Experimentation

Conduct experiments to evaluate model performance and compare different approaches.

Use appropriate evaluation metrics, including F1-score, AUC, precision, recall, and precision-recall curves.

Apply BLEU and ROUGE metrics where relevant to NLP and text generation tasks.

Analyse model errors, document results, and identify opportunities for improvement.

Maintain reproducible experimentation and model evaluation workflows.

Cloud Platforms, Deployment & MLOps

Use cloud-based ML platforms, including Amazon SageMaker, for model development, training, and deployment.

Use Docker for containerisation, MLflow for experiment tracking and model management, and Git for version control.

Collaborate with data scientists and software engineers to integrate models into production systems.

Support model testing, deployment, monitoring, and performance optimisation.

Ensure AI/ML solutions are scalable, maintainable, and reliable.

Code Quality, Collaboration & Responsible AI

Write clean, reusable, maintainable, and well-documented code.

Follow development best practices, including testing, version control, and code reviews.

Collaborate with cross-functional teams to understand requirements and implement AI/ML solutions.

Document model architecture, training procedures, experiments, and evaluation results.

Follow responsible AI principles, address potential bias, and ensure appropriate data protection.

Key Deliverables

Developed and optimised machine learning and deep learning models.

Reliable data preprocessing and feature engineering pipelines.

Fine-tuned models for NLP, Computer Vision, or Predictive Analytics.

Documented experiments and reproducible training workflows.

Model evaluation reports with appropriate performance metrics.

Improved model accuracy, reliability, and inference efficiency.

Maintainable code, technical documentation, and model configurations.

Successful collaboration on model deployment and production integration.

Compliance with responsible AI and data protection practices.

Performance Metrics

Model accuracy and performance against defined objectives.

Improvements in model performance following fine-tuning and optimisation.

Efficiency of model training and inference.

Quality of preprocessing and feature engineering pipelines.

Reproducibility of experiments and evaluation results.

Quality of code, documentation, and version control.

Successful model integration and deployment.

Model stability, reliability, and maintainability.

Timely delivery of assigned tasks.

Candidate Requirements

4-7 years of hands-on experience in AI/ML model development, training, fine-tuning, and optimisation.

B.Tech./M.Tech. in Computer Science, Artificial Intelligence, Data Science, or an equivalent field.

Practical experience in NLP, Computer Vision, or Predictive Analytics.

Experience implementing AI/ML solutions using open-source frameworks or cloud platforms.

Strong programming knowledge of Python, C++, and R.

Experience with PyTorch, TensorFlow, Keras, and Hugging Face.

Familiarity with pandas, NumPy, OpenCV, spaCy, and Transformers.

Experience with SQL, NoSQL, data preprocessing, and data augmentation.

Knowledge of Docker, Amazon SageMaker, MLflow, and Git.

Understanding of evaluation metrics, including F1-score, BLEU, ROUGE, AUC, precision, recall, and precision-recall curves.

Experience conducting experiments, debugging errors, and improving model performance.

Understanding of model deployment and production integration.

Knowledge of responsible AI, model reliability, and data protection.

Strong analytical, problem-solving, and debugging skills.

Good technical communication and documentation skills.

Preferred Qualifications

Certifications in machine learning, artificial intelligence, or deep learning.

Research papers, technical publications, or documented AI/ML case studies.

Contributions to open-source AI/ML projects.

Experience building and deploying end-to-end AI/ML solutions.

Required Skills

Artificial Intelligence | Machine Learning | Deep Learning | Model Development | Model Fine-Tuning | Model Optimisation | Python | C++ | R | PyTorch | TensorFlow | Keras | Hugging Face | pandas | NumPy | OpenCV | spaCy | Transformers | NLP | Computer Vision | Predictive Analytics | SQL | NoSQL | Data Preprocessing | Feature Engineering | Data Augmentation | Amazon SageMaker | Docker | MLflow | Git | Model Evaluation | F1-Score | BLEU | ROUGE | AUC | Precision & Recall | Experiment Tracking | Model Deployment | MLOps | Responsible AI | Data Protection | Debugging

Core Competencies

Strong AI/ML development and model fine-tuning expertise. Good understanding of machine learning and deep learning algorithms. Ability to develop efficient preprocessing and feature engineering pipelines. Practical knowledge of NLP, Computer Vision, or Predictive Analytics. Strong programming and debugging capabilities. Ability to evaluate models and improve accuracy and latency. Understanding of cloud-based ML platforms and MLOps tools. Commitment to reproducible experiments, code quality, and responsible AI development.

What We Offer

CTC of ₹14-18 LPA, depending on experience and suitability.

Opportunity to work on AI/ML model development and optimisation.

Exposure to machine learning frameworks, cloud platforms, and modern AI tools.

Opportunity to work on NLP, Computer Vision, and Predictive Analytics projects.

Professional growth in AI/ML engineering and production-oriented model development.

How to Apply

Interested candidates can share their updated resume at:

Email: khushi@hirednex.com