SENIOR PYTHON AI/ML ENGINEER

Key Responsibilities Design, develop, test, and deploy AI/ML and Generative AI solutions for enterprise applications.Build end-to-end ML pipelines covering data ingestion, feature engineering, model training, evaluation, deployment, and monitoring.Develop RAG solutions and integrate large language models into enterprise systems.Build scalable Python applications, backend services, microservices, and RESTful APIs.Design data pipelines and integrate structured and unstructured data across enterprise platforms.Implement CI/CD pipelines, automated testing, and secure deployment workflows.Apply MLOps practices, including model versioning, experiment tracking, monitoring, drift detection, and retraining.Deploy cloud-native applications using AWS, Azure, or GCP, with Docker and Kubernetes.Collaborate with data engineering, platform, and business teams to ensure solution quality, security, scalability, and reliability. Required Qualifications Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field, or equivalent experience.8–12 years of software engineering experience, including substantial hands-on Python development.Production experience developing and deploying AI/ML solutions.Strong understanding of machine learning algorithms, model evaluation, and MLOps principles.Experience building RESTful APIs, microservices, and enterprise integrations.Hands-on experience with at least one major cloud platform: AWS, Azure, or GCP.Experience with CI/CD pipelines, DevOps practices, Docker, Kubernetes, SQL, Git, and automated testing.Strong debugging, analytical, communication, and problem-solving skills. Preferred Qualifications Experience with Generative AI, LLMs, RAG, prompt engineering, embeddings, and vector databases.Familiarity with LangChain, LlamaIndex, Semantic Kernel, scikit-learn, TensorFlow, or PyTorch.Experience with Snowflake, Databricks, Spark, PySpark, MLflow, Kubeflow, or Airflow.Experience developing APIs using FastAPI or Flask.Knowledge of responsible AI, model governance, data privacy, and enterprise security requirements.Experience in financial services or other regulated enterprise environments.