AI/ML Engineer

AI/ML Engineer
Role Overview
We are looking for a Machine Learning Engineer strongly aligned to model development, experimentation, training, evaluation, and productionization of AI/ML solutions. The ideal candidate should have hands-on experience building ML and deep learning models and working across traditional/legacy models, modern AI models, and frontier/foundation models. Strong Python and MLOps experience is required.
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.

Work arrangement
No