AI Platform Engineer

We are looking for an experienced AI Engineer with 4-8 years of experience in Machine Learning, Artificial Intelligence, and Generative AI. The ideal candidate will design, develop, and deploy scalable AI solutions across traditional machine learning and GenAI use cases.

Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Engineering, or related field. 8-10 years of experience in AI/ML or Data Science roles. Strong Python programming and AWS experience.

Build end-to-end ML solutions for classification, regression, recommendation, forecasting, clustering, anomaly detection, and optimization problems. Perform feature engineering, model experimentation, hyperparameter tuning, and model evaluation. Design and execute A/B testing and model validation strategies. Develop explainable and auditable ML models for business-critical applications. Monitor model performance and support retraining initiatives. Design and develop LLM-powered applications. Build RAG systems using vector databases and enterprise knowledge repositories. Develop AI copilots, intelligent assistants, and agentic workflows. Implement prompt engineering, prompt optimization, evaluation frameworks, and guardrails. Develop production-grade AI services, APIs, SDKs, and microservices. Design scalable REST APIs and event-driven architectures. Apply design patterns, SOLID principles, and clean coding standards. Work with Docker, EKS, SNS, SQS, and other AWS services. Must-Have Skills Python Strong hands-on experience with Python for developing production-grade AI and machine learning applications. Ability to write clean, maintainable, and reusable code following software engineering best practices. Experience developing APIs, automation frameworks, and AI services using Python. SQL & Data Analysis Strong proficiency in SQL for data extraction, transformation, and analysis. Experience working with large-scale structured and semi-structured datasets. Ability to perform exploratory data analysis and derive business insights from data. Machine Learning Strong expertise in machine learning algorithms and techniques including: Classification Regression Clustering Recommendation Systems Forecasting Anomaly Detection Hands-on experience with: Scikit-Learn XGBoost LightGBM CatBoost Deep understanding of: Feature Engineering Model Evaluation Hyperparameter Tuning Cross Validation Explainable AI Deep Learning & NLP Practical experience developing NLP solutions using TensorFlow or PyTorch. Understanding of transformers, embeddings, and modern NLP techniques. Experience with text classification, semantic search, summarization, information extraction, and conversational AI use cases. Generative AI Hands-on experience building enterprise-grade GenAI applications. Strong understanding of: Large Language Models (LLMs) Prompt Engineering Retrieval-Augmented Generation (RAG) Agentic AI Workflows Structured Output Generation Evaluation Frameworks Experience using frameworks such as: LangChain LangGraph API & Software Engineering Experience designing and developing RESTful APIs and microservices. Strong understanding of design patterns, object-oriented programming, and SOLID principles. Experience creating reusable AI components, SDKs, and shared libraries. AWS Cloud Technologies Working knowledge of AWS services commonly used for AI applications including: Amazon EKS SNS SQS Lambda S3 API Gateway Ability to build scalable, cloud-native AI solutions. Containerization Experience with Docker for packaging and deploying AI applications. Understanding of container-based application development and deployment. Version Control Strong experience with Git and collaborative development workflows including code reviews, branching strategies, and release management.   Good-to-Have Skills Apache Spark Experience processing large-scale datasets using Spark. Understanding of distributed data processing