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Metro location, broad ML/GenAI requirements, and multinational employer create moderate competition density.
Medium because core ML/GenAI skills transfer across industries but platform and cloud specifics increase domain specificity.
High due to explicit 8–10 years requirement and many mandatory ML, GenAI, cloud, and production skills.
Design, develop, and deploy scalable end-to-end AI/ML solutions including classification, regression, recommendation, forecasting, clustering, anomaly detection, and optimization.
Develop and maintain production-grade AI services, APIs, microservices, and LLM-powered applications including RAG systems and agentic AI workflows.
Monitor model performance, perform feature engineering, model tuning, validation, and implement prompt engineering, evaluation frameworks, and guardrails in GenAI systems.
4-8 years experience in Machine Learning, Artificial Intelligence, and Generative AI (though 8-10 years mentioned elsewhere - note conflict).
Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Engineering, or related field.
Strong programming skills in Python with experience developing production AI applications, REST APIs, and microservices.
Experience with AWS services (EKS, SNS, SQS, Lambda, S3, API Gateway) and containerization using Docker.
Practitioner with strong expertise in classical machine learning algorithms and practical experience with frameworks like Scikit-Learn, XGBoost, LightGBM, CatBoost.
Experienced in developing NLP and Generative AI solutions involving transformers, embeddings, LangChain, and RAG architectures.
Skilled in software engineering best practices including clean code, design patterns, object-oriented design, SOLID principles, version control, and cloud-native deployments on AWS.