Data Scientist/AI Engineer

You are a hands-on Data Scientist/AI Engineer with expertise in AI/ML and Generative AI (GenAI), particularly in developing and implementing real-world solutions on large-scale datasets. Your role is delivery-focused, requiring experience in working on LLM-based applications beyond the prototype stage and deploying AI systems successfully into production environments. You will be working on high-scale data systems, production ML pipelines, and GenAI-powered applications to enhance analytics and intelligent product features. Key Responsibilities: - Design, develop, and deploy LLM-based and GenAI solutions for real-world use cases - Build RAG pipelines, LLM workflows, and AI-powered assistants - Perform prompt engineering, model evaluation, and fine-tuning - Develop and optimize advanced machine learning models for prediction and analytics - Work with large-scale structured and unstructured datasets - Build scalable inference systems and APIs for model deployment - Integrate AI/ML solutions with existing products and data platforms - Improve model performance, latency, and cost efficiency - Collaborate closely with engineering and product teams to productionize AI features - Monitor, evaluate, and continuously improve deployed models Qualifications Required: - Strong proficiency in Python and the ML ecosystem - Hands-on experience in building LLM and Generative AI applications - Expertise in prompt engineering, RAG architectures, embeddings, and vector search - Experience with frameworks such as LangChain, LlamaIndex, or similar tools - Familiarity with vector databases like FAISS, Milvus, or Pinecone - Practical experience with PyTorch or TensorFlow - Experience working with large-scale and distributed datasets - Proven experience in deploying ML/AI models as APIs or production services - Strong SQL and data analysis skills - Understanding of ML lifecycle, evaluation, and monitoring - Exposure to MLOps practices and experiment tracking tools - Experience with cloud platforms (AWS/GCP/Azure) is preferred - Familiarity with streaming or high-volume telemetry data systems is an added advantage You are an ideal candidate if you have: - Successfully deployed at least one LLM/GenAI solution in production - Demonstrated a strong problem-solving and execution mindset - Comfortable working in fast-paced, ambiguous environments - Focused on scalable, practical AI solutions rather than just experimentation - Able to translate business problems into effective AI-driven solutions - Strong communication and collaboration skills You are a hands-on Data Scientist/AI Engineer with expertise in AI/ML and Generative AI (GenAI), particularly in developing and implementing real-world solutions on large-scale datasets. Your role is delivery-focused, requiring experience in working on LLM-based applications beyond the prototype stage and deploying AI systems successfully into production environments. You will be working on high-scale data systems, production ML pipelines, and GenAI-powered applications to enhance analytics and intelligent product features. Key Responsibilities: - Design, develop, and deploy LLM-based and GenAI solutions for real-world use cases - Build RAG pipelines, LLM workflows, and AI-powered assistants - Perform prompt engineering, model evaluation, and fine-tuning - Develop and optimize advanced machine learning models for prediction and analytics - Work with large-scale structured and unstructured datasets - Build scalable inference systems and APIs for model deployment - Integrate AI/ML solutions with existing products and data platforms - Improve model performance, latency, and cost efficiency - Collaborate closely with engineering and product teams to productionize AI features - Monitor, evaluate, and continuously improve deployed models Qualifications Required: - Strong proficiency in Python and the ML ecosystem - Hands-on experience in building LLM and Generative AI applications - Expertise in prompt engineering, RAG architectures, embeddings, and vector search - Experience with frameworks such as LangChain, LlamaIndex, or similar tools - Familiarity with vector databases like FAISS, Milvus, or Pinecone - Practical experience with PyTorch or TensorFlow - Experience working with large-scale and distributed datasets - Proven experience in deploying ML/AI models as APIs or production services - Strong SQL and data analysis skills - Understanding of ML lifecycle, evaluation, and monitoring - Exposure to MLOps practices and experiment tracking tools - Experience with cloud platforms (AWS/GCP/Azure) is preferred - Familiarity with streaming or high-volume telemetry data systems is an added advantage You are an ideal candidate if you have: - Successfully deployed at least one LLM/GenAI solution in production - Demonstrated a strong problem-solving and execution mindset - Comfortable working in fast-paced, ambiguous environments - Focused on scalable, practical AI solutions rather than ju