LTM - Data Science Specialist - Machine Learning/Generative AI

Job Summary : We are looking for a highly skilled and experienced Data Science Specialist with strong expertise in Python, Traditional Machine Learning, and Generative AI technologies. The ideal candidate should have hands-on experience in building, deploying, and evaluating production-grade Gen AI solutions along with a solid understanding of statistical concepts, MLOps practices, and cloud-native deployment environments. Key Responsibilities : - Design, develop, and deploy scalable machine learning and Generative AI solutions for enterprise use cases. - Build and optimize traditional machine learning models using Python and relevant ML frameworks. - Work on production-grade Gen AI applications including Retrieval-Augmented Generation (RAG) systems and LLM-powered solutions. - Develop and implement evaluation methodologies for Gen AI systems using benchmark datasets and ground truth-based evaluation techniques. - Utilize evaluation frameworks such as RAGAS, DeepEval, and LLM-as-a-Judge approaches to assess model quality, relevance, and performance. - Collaborate with cross-functional teams including engineering, product, and business stakeholders to understand requirements and deliver AI-driven solutions. - Build APIs and deploy AI/ML services using FastAPI and containerized environments. - Manage deployments using Docker and Kubernetes for scalable and reliable production environments. - Work on CI/CD pipelines to automate model deployment, testing, and monitoring workflows. - Monitor and maintain LLM applications using observability and monitoring tools to ensure performance, reliability, and governance. - Perform statistical analysis and data preprocessing to improve model accuracy and business outcomes. - Stay updated with the latest advancements in AI/ML, Generative AI, and MLOps technologies. Required Skills & Qualifications : - 6 to 8 years of experience in Data Science, Machine Learning, or AI-related roles. - Strong programming expertise in Python. - Hands-on experience with traditional Machine Learning algorithms and frameworks. - Strong understanding of statistics and data analysis concepts. - Proven experience in delivering production-grade Generative AI projects. - Familiarity with Gen AI evaluation frameworks such as RAGAS, DeepEval, and LLM adjudication methodologies. - Experience with benchmark dataset evaluation and ground truth validation techniques. - Strong knowledge of Docker, Kubernetes, and containerized application deployment. - Experience in developing APIs using FastAPI. - Understanding of CI/CD pipelines and DevOps practices. - Exposure to LLM monitoring and observability platforms/tools. - Strong analytical, problem-solving, and communication skills. Preferred Qualifications : - Experience with cloud platforms such as AWS, Azure, or GCP. - Understanding of vector databases, embeddings, and RAG architectures. - Familiarity with MLOps and AI governance practices. - Exposure to enterprise-scale AI deployments and performance optimization. Job Summary : We are looking for a highly skilled and experienced Data Science Specialist with strong expertise in Python, Traditional Machine Learning, and Generative AI technologies. The ideal candidate should have hands-on experience in building, deploying, and evaluating production-grade Gen AI solutions along with a solid understanding of statistical concepts, MLOps practices, and cloud-native deployment environments. Key Responsibilities : - Design, develop, and deploy scalable machine learning and Generative AI solutions for enterprise use cases. - Build and optimize traditional machine learning models using Python and relevant ML frameworks. - Work on production-grade Gen AI applications including Retrieval-Augmented Generation (RAG) systems and LLM-powered solutions. - Develop and implement evaluation methodologies for Gen AI systems using benchmark datasets and ground truth-based evaluation techniques. - Utilize evaluation frameworks such as RAGAS, DeepEval, and LLM-as-a-Judge approaches to assess model quality, relevance, and performance. - Collaborate with cross-functional teams including engineering, product, and business stakeholders to understand requirements and deliver AI-driven solutions. - Build APIs and deploy AI/ML services using FastAPI and containerized environments. - Manage deployments using Docker and Kubernetes for scalable and reliable production environments. - Work on CI/CD pipelines to automate model deployment, testing, and monitoring workflows. - Monitor and maintain LLM applications using observability and monitoring tools to ensure performance, reliability, and governance. - Perform statistical analysis and data preprocessing to improve model accuracy and business outcomes. - Stay updated with the latest advancements in AI/ML, Generative AI, and MLOps technologies. Required