Ai Ml Engineer

Role & responsibilities

Key Responsibilities

• Design, develop, train, evaluate, and deploy machine learning and deep learning models.
• Own the ML lifecycle from data preparation and feature engineering through model development, evaluation, deployment, and monitoring.
• Select appropriate algorithms and architectures based on business and technical requirements.
• Develop models using Python, TensorFlow, PyTorch, scikit-learn, and related ML frameworks.
• Work with traditional ML models as well as modern deep learning and foundation-model architectures.
• Explore and integrate frontier models, foundation models, and emerging AI models into enterprise AI solutions.
• Work with legacy/traditional AI/ML models where existing business solutions need to be enhanced, migrated, or integrated.
• Perform experimentation, hyperparameter tuning, benchmarking, and performance optimization.
• Build evaluation frameworks for ML, deep learning, and GenAI models.
• Develop production-ready ML pipelines and integrate models into enterprise applications.
• Implement MLOps practices covering model versioning, experiment tracking, CI/CD, deployment, monitoring, and model lifecycle management.
• Work with cloud AI/ML platforms, preferably Microsoft Azure.
• Collaborate with data engineers, software engineers, architects, and product teams to productionize AI solutions.

Required Technical Skills

• Python strong hands-on development experience.
• Machine Learning: supervised/unsupervised learning, classification, regression, clustering, recommendation systems, feature engineering, and model evaluation.
• Deep Learning: neural networks, CNNs, RNNs/LSTMs, Transformers, attention mechanisms, and modern deep learning architectures.
• Frameworks: TensorFlow/Keras, PyTorch, scikit-learn, NumPy, and Pandas.
• Generative AI: LLMs, foundation models, embeddings, RAG, fine-tuning, prompt engineering, model evaluation, and AI agents.
• Understanding of frontier models, foundation models, modern LLM architectures, and traditional/legacy ML models.
• MLOps: ML pipelines, experiment tracking, model registry, model versioning, deployment, monitoring, and CI/CD.
• Cloud: Azure preferred; AWS/GCP experience is also valuable.
• Data: SQL, data preprocessing, feature engineering, distributed data processing, and large datasets.
• Software Engineering: Git, APIs, Docker, testing, and production deployment.

Good to Have

• Azure Machine Learning / Azure AI experience.
• Azure OpenAI or experience with other foundation-model platforms.
• Databricks and MLflow experience.
• LLM fine-tuning, LoRA/PEFT, quantization, or model optimization.
• Vector databases and retrieval systems.
• Responsible AI, model governance, explainability, and model security.
• Kubernetes and cloud-native ML deployments.
• GPU-based model training and inference optimization.
• Research experience or exposure to recent developments in Generative AI and Deep Learning.

Experience

5+ years of relevant ML/AI experience, with substantial hands-on experience in model development, deep learning, and production ML/MLOps.