AI/ML Engineer

We are looking for a hands-on AI/ML Engineer with 4+ years of experience who can independently design, develop, train, evaluate, optimize, and deploy machine learning models from scratch. The ideal candidate should have strong foundations in Machine Learning, Deep Learning, Statistics, and Python programming, along with proven experience in taking ML models from experimentation to production.

The candidate must have practical experience building end-to-end machine learning solutions, including data preprocessing, feature engineering, model training, performance optimization, deployment, and monitoring.

Key Responsibilities

• Design, develop, and implement machine learning models from scratch based on business requirements.
• Perform data collection, cleaning, preprocessing, exploratory data analysis, and feature engineering.
• Select appropriate algorithms and develop supervised and unsupervised machine learning models.
• Implement regression, classification, clustering, and ensemble learning techniques.
• Develop Deep Learning, NLP, and Generative AI solutions based on project requirements.
• Build end-to-end ML pipelines covering data preparation, model training, validation, deployment, and monitoring.
• Perform hyperparameter tuning, model optimization, error analysis, and performance benchmarking.
• Evaluate model performance using appropriate statistical metrics and validation techniques.
• Conduct A/B testing and experimentation to improve model accuracy and business outcomes.
• Develop production-ready ML solutions through REST APIs, batch processing, or real-time inference systems.
• Implement MLOps practices, including experiment tracking, model versioning, CI/CD, and model monitoring.
• Collaborate with Data Engineers, Software Engineers, Product Managers, and business stakeholders.
• Optimize models for scalability, reliability, performance, and production environments.
• Research emerging AI/ML techniques and assess their applicability to business use cases.

Mandatory Technical Skills

• Minimum 4+ years of hands-on experience in AI/ML development.
• Strong programming expertise in Python.
• Strong understanding of Machine Learning fundamentals, Statistics, and Probability.
• Hands-on experience with:
• Regression and Classification
• Clustering and Unsupervised Learning
• Ensemble Methods
• Feature Engineering and Feature Selection
• Model Evaluation, Validation, and Optimization
• Strong experience with Python libraries such as Scikit-learn, Pandas, and NumPy.
• Practical experience with PyTorch and/or TensorFlow for Deep Learning model development.
• Proven ability to design, build, train, and fine-tune ML models rather than only consuming pre-trained models or third-party AI APIs.
• Experience in hyperparameter tuning, cross-validation, and model performance optimization.
• Strong SQL skills and experience handling structured and unstructured datasets.
• Experience building end-to-end ML pipelines and deploying production-ready models.
• Understanding of model deployment, inference, monitoring, and maintenance in production environments.

Good-to-Have Skills

• Experience with NLP, Large Language Models (LLMs), and Generative AI.
• Familiarity with Transformers, Hugging Face, LangChain, or similar frameworks.
• Experience in Computer Vision and image-processing applications.
• Cloud experience with AWS, Microsoft Azure, or Google Cloud Platform (GCP).
• Experience with Docker and Kubernetes.
• Familiarity with MLOps tools such as MLflow, Kubeflow, or equivalent platforms.
• Experience developing REST APIs using FastAPI or Flask.
• Experience with Apache Spark or PySpark for large-scale data processing.
• Knowledge of model serving, distributed training, and scalable inference systems.

Ideal Candidate Profile

• Strong problem-solving and analytical skills.
• Ability to independently own the complete ML development lifecycle.
• Strong understanding of the mathematical and statistical foundations of machine learning.
• Hands-on experience translating business problems into effective ML solutions.
• Ability to balance model accuracy, computational efficiency, scalability, and maintainability.
• Strong communication and collaboration skills.

Skills: python,machine learning,models