Skills
• Results-driven AI Engineer around 4 years of experience building and deploying production-grade Generative AI systems, agentic AI workflows, RAG
pipelines, and multimodal deep learning solutions across financial services and conversational AI domains.
• Strong expertise in Natural Language Processing (NLP), Generative AI, Computer Vision, and Speech AI, with hands-on experience in transformer
based models like BERT and GPT.
• Proficient in designing RAG pipelines using LangChain, LlamaIndex, and Pinecone/FAISS vector stores, and implementing multimodal deep learning
systems with quantified accuracy improvements of 20–30% and latency reductions of up to 40%.
• Extensive experience in training, fine-tuning, and optimizing large-scale models using PyTorch, TensorFlow, and Hugging Face Transformers.
• Skilled in building end-to-end MLOps pipelines, including CI/CD integration, experiment tracking with MLflow, and deployment using Docker and
Kubernetes.
• Strong experience with cloud-native deployments on Amazon Web Services, including GPU-accelerated training and scalable production systems.
• Expertise in large-scale data processing using Apache Spark and Databricks, focusing on performance optimization and high availability.
• Proven ability to improve model accuracy by 20–30% and reduce inference latency by up to 40% through optimization and efficient system design.
• Strong collaboration skills, working with cross-functional teams to translate business requirements into scalable AI solutions.
• Passionate about building secure, scalable, and responsible AI systems that bridge advanced research with real-world applications.
About
• Results-driven AI Engineer around 4 years of experience building and deploying production-grade Generative AI systems, agentic AI workflows, RAG
pipelines, and multimodal deep learning solutions across financial services and conversational AI domains.
• Strong expertise in Natural Language Processing (NLP), Generative AI, Computer Vision, and Speech AI, with hands-on experience in transformer
based models like BERT and GPT.
• Proficient in designing RAG pipelines using LangChain, LlamaIndex, and Pinecone/FAISS vector stores, and implementing multimodal deep learning
systems with quantified accuracy improvements of 20–30% and latency reductions of up to 40%.
• Extensive experience in training, fine-tuning, and optimizing large-scale models using PyTorch, TensorFlow, and Hugging Face Transformers.
• Skilled in building end-to-end MLOps pipelines, including CI/CD integration, experiment tracking with MLflow, and deployment using Docker and
Kubernetes.
• Strong experience with cloud-native deployments on Amazon Web Services, including GPU-accelerated training and scalable production systems.
• Expertise in large-scale data processing using Apache Spark and Databricks, focusing on performance optimization and high availability.
• Proven ability to improve model accuracy by 20–30% and reduce inference latency by up to 40% through optimization and efficient system design.
• Strong collaboration skills, working with cross-functional teams to translate business requirements into scalable AI solutions.
• Passionate about building secure, scalable, and responsible AI systems that bridge advanced research with real-world applications.
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