An AI/ML Engineer is responsible for designing, building, training, deploying, and maintaining machine learning and artificial intelligence systems that solve complex, real-world problems. The role bridges data science and software engineering, focusing on transforming data and models into scalable, production-ready solutions.

Key Responsibilities

• Design, develop, and optimize machine learning and deep learning models for prediction, classification, NLP, and computer vision tasks

• Collect, clean, preprocess, and analyze large structured and unstructured datasets

• Perform feature engineering, model selection, hyperparameter tuning, and performance evaluation

• Deploy ML models into production using APIs, cloud platforms, or edge environments

• Monitor model performance, handle model drift, and continuously improve accuracy and efficiency

• Collaborate with cross-functional teams including product managers, data scientists, and software engineers

• Ensure models follow best practices for security, scalability, ethics, and data privacy

• Stay updated with latest AI/ML research, frameworks, and industry trends

Required Skills

Programming & Tools

• Python (NumPy, Pandas, Scikit-learn)

• Deep learning frameworks: TensorFlow, PyTorch, Keras

• Data visualization tools: Matplotlib, Seaborn

• SQL and NoSQL databases

• Git, Docker, CI/CD pipelines

Machine Learning & AI

• Supervised and unsupervised learning

• Deep learning (CNNs, RNNs, Transformers)

• Natural Language Processing (NLP)

• Computer Vision

• Model evaluation metrics and optimization techniques

• Feature engineering and data preprocessing

MLOps & Deployment

• Model deployment using REST APIs (FastAPI, Flask)

• Cloud platforms (AWS, GCP, Azure)

• Model monitoring, versioning, and retraining

• Experience with ML pipelines and workflow orchestration

Experience & Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or related field

• 2–5+ years of experience working with machine learning models in real-world applications (can be adjusted)

• Hands-on experience deploying ML models to production environments

• Experience working with large datasets and distributed systems is a plus

• Strong understanding of algorithms, data structures, and statistics

Soft Skills

• Strong problem-solving and analytical thinking

• Ability to translate business problems into ML solutions

• Clear communication and collaboration skills

• Continuous learning mindset and adaptability

Preferred / Nice-to-Have

• Experience with LLMs, generative AI, and prompt engineering

• Knowledge of reinforcement learning

• Experience with big data tools (Spark, Hadoop)

• Research publications or open-source contributions