ML Engineer

We are seeking a talented AI/ML Engineer to design, develop, deploy, and maintain machine learning models and AI-powered applications. The ideal candidate will have strong expertise in Python, machine learning algorithms, data processing, and model deployment. You will work closely with data scientists, software engineers, and business stakeholders to build scalable AI solutions that solve real-world business problems., · Design, develop, train, and optimize machine learning and deep learning models for production use.

· Build end-to-end machine learning pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment.

· Collaborate with cross-functional teams to understand business requirements and translate them into AI/ML solutions.

· Deploy, monitor, and maintain machine learning models in production environments.

· Develop scalable AI services and APIs using Python and modern ML frameworks.

· Evaluate and improve model performance using appropriate metrics and optimization techniques.

· Perform data analysis, experimentation, and model validation to ensure high-quality predictions.

· Work with structured and unstructured data from various sources.

· Implement MLOps best practices for versioning, automation, model monitoring, and continuous improvement.

· Optimize model inference performance for scalability and reliability.

· Document model architectures, development processes, and technical solutions.

· Stay current with the latest advancements in artificial intelligence, machine learning, and generative AI technologies.

· Bachelor’’s or Master’’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.

· Strong proficiency in Python programming.

· Hands-on experience with machine learning and deep learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.

· Solid understanding of supervised, unsupervised, and reinforcement learning techniques.

· Experience with data preprocessing, feature engineering, model training, and evaluation.

· Experience deploying machine learning models into production environments.

· Familiarity with MLOps concepts, including model versioning, monitoring, and lifecycle management.

· Experience with REST APIs and integrating AI models into applications.

· Knowledge of SQL and experience working with relational and NoSQL databases.

· Familiarity with Docker containers and Kubernetes for model deployment.

· Experience with cloud platforms such as AWS, Azure, or Google Cloud.

· Understanding of software engineering best practices, including Git, testing, and CI/CD.

· Strong analytical, problem-solving, and communication skills.

Preferred Qualifications

· Experience working with Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), or AI agents.

· Experience with vector databases such as Pinecone, Weaviate, or Milvus.

· Knowledge of NLP, computer vision, or time-series forecasting.

· Familiarity with ML experiment tracking tools such as MLflow or Weights & Biases.

· Experience with distributed model training and GPU acceleration.

· Contributions to open-source AI/ML projects or published research are a plus.