AI/ML Engineer

Key Responsibilities

• Design, develop, and implement machine learning and AI models for real-world business applications.
• Analyze large and complex datasets to identify patterns, trends, correlations, and opportunities for predictive modeling.
• Develop and optimize supervised, unsupervised, and semi-supervised machine learning algorithms.
• Build end-to-end ML pipelines, including data preparation, feature engineering, model development, validation, deployment, and monitoring.
• Apply statistical methods and data science techniques to solve business and technical problems.
• Evaluate model performance using appropriate metrics and continuously improve accuracy, scalability, and efficiency.
• Work with structured and unstructured data, including text, images, and other high-dimensional datasets.
• Collaborate with engineering teams to integrate AI/ML models into production applications and APIs.
• Develop scalable solutions using cloud platforms, distributed computing frameworks, and modern ML infrastructure.
• Stay updated with advancements in AI, Generative AI, deep learning, NLP, computer vision, and MLOps.
• Conduct experiments, proof-of-concepts, and technical evaluations for emerging AI/ML technologies.
• Document model architectures, methodologies, experiments, results, and deployment processes.

Must-Have Skills

• AI: Strong understanding of Artificial Intelligence concepts, algorithms, and practical applications.
• Machine Learning: Deep hands-on experience with machine learning algorithms, model development, optimization, and deployment.
• Data Science: Strong expertise in data analysis, statistical modeling, data preprocessing, feature engineering, and exploratory data analysis.
• Strong programming skills in Python and familiarity with libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, or PyTorch.
• Experience developing and deploying production-grade machine learning models.
• Strong understanding of model evaluation, validation, feature selection, hyperparameter tuning, and optimization.
• Experience working with SQL and large datasets.
• Good understanding of data structures, algorithms, and software engineering principles.
• Experience with cloud environments such as AWS, Azure, or GCP.
• Strong analytical, problem-solving, and communication skills.

Good to Have

• Experience with Generative AI, LLMs, NLP, or Computer Vision.
• Knowledge of MLOps, model monitoring, CI/CD, Docker, Kubernetes, or MLflow.
• Experience with distributed data processing technologies such as Spark.
• Familiarity with vector databases, embeddings, RAG architectures, and AI application development.
• Experience deploying scalable AI/ML services in production.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related field.
• 6–10 years of relevant professional experience in AI/ML, Machine Learning Engineering, or Data Science.
• Demonstrated experience building and deploying AI/ML solutions in production environments.