AI/ML Engineer

Key ResponsibilitiesDesign, develop, train, evaluate, and deploy machine learning and deep learning models.Collect, clean, transform, and analyze structured and unstructured datasets.Perform feature engineering, data preprocessing, model selection, and hyperparameter tuning.Develop supervised, unsupervised, and semi-supervised machine learning solutions.Work with algorithms for classification, regression, clustering, recommendation, and anomaly detection.Develop and integrate Generative AI, Large Language Models (LLMs), NLP, and computer vision solutions where applicable.Build AI/ML pipelines for model training, validation, deployment, and monitoring.Implement MLOps practices for model versioning, CI/CD, deployment, monitoring, and retraining.Deploy models using cloud platforms, APIs, containers, and scalable infrastructure.Optimize models for performance, scalability, accuracy, latency, and resource utilization.Conduct experiments and analyze model performance using appropriate evaluation metrics.Collaborate with data engineers, software developers, data scientists, product teams, and business stakeholders.Translate business requirements into practical AI/ML solutions.Maintain technical documentation for models, datasets, experiments, APIs, and deployment processes.Monitor production models for performance degradation, data drift, and model drift.Stay current with developments in AI, ML, deep learning, Generative AI, and emerging technologies.Required Technical SkillsStrong programming skills in Python.Good understanding of Machine Learning and Deep Learning concepts.Hands-on experience with frameworks such as:Scikit-learnPyTorchTensorFlow/KerasStrong knowledge of NumPy, Pandas, Matplotlib/Seaborn, and related data-processing libraries.Understanding of statistics, probability, linear algebra, and optimization.Experience with SQL and working with relational databases.Knowledge of data preprocessing, feature engineering, model evaluation, and validation techniques.Familiarity with REST APIs and software development practices.Knowledge of Git, Docker, and CI/CD.Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud is preferred.Generative AI / LLM SkillsUnderstanding of Generative AI and Large Language Models (LLMs).Experience with LLM APIs and/or open-source models.Knowledge of prompt engineering and LLM evaluation.Experience building RAG (Retrieval-Augmented Generation) applications is preferred.Familiarity with vector databases and embeddings.Exposure to frameworks such as LangChain, LlamaIndex, or similar is an advantage.Understanding of model fine-tuning, LoRA/PEFT, and model optimization is a plus.Awareness of responsible AI, security, privacy, and AI governance principles.MLOps & Cloud SkillsExperience deploying ML models into production environments.Knowledge of Docker and Kubernetes is an advantage.Familiarity with ML platforms such as MLflow, Kubeflow, SageMaker, Azure ML, or Vertex AI.Understanding of CI/CD pipelines and automated model deployment.Experience with cloud storage, compute, databases, and monitoring services.Knowledge of model monitoring, data drift, model drift, and performance monitoring.Soft SkillsStrong analytical and problem-solving skills.Excellent programming and debugging abilities.Ability to convert business problems into scalable AI/ML solutions.Strong communication and collaboration skills.Ability to work independently as well as within cross-functional teams.Strong curiosity and willingness to learn emerging AI technologies.Good documentation and presentation skills.