4+ years of experience building scalable AI/ML platforms, Generative AI applications, cloud-native systems, and distributed ML infrastructure using Python, AWS, Kubernetes, and modern ML frameworks. Strong experience des…
4+ years of experience building scalable AI/ML platforms, Generative AI applications, cloud-native systems, and distributed ML infrastructure using Python, AWS, Kubernetes, and modern ML frameworks. Strong experience designing and deploying Retrieval-Augmented Generation (RAG) systems, LLM-powered applications, vector search platforms, and agentic AI workflows using LangChain, LangGraph, FAISS, and FastAPI. Hands-on experience productionizing machine learning systems including model training, inference optimization, real-time evaluation pipelines, monitoring, observability, and automated deployment workflows. Experience building scalable cloud-native ML infrastructure using AWS, Kubernetes (EKS), Docker, Terraform, Helm, and event-driven architectures supporting low-latency AI applications. Skilled in distributed systems, asynchronous processing, Redis caching, streaming architectures, and scalable backend engineering for AI-powered enterprise applications. Experience working with ML lifecycle workflows including data ingestion, feature engineering, embeddings, vector databases, model serving, evaluation pipelines, and operational monitoring. Strong knowledge of AI/ML security practices including OAuth2.0, JWT authentication, RBAC, secure API development, secrets management, and enterprise deployment standards. Familiarity with transformer architectures, LLM evaluation techniques, AI safety concepts, prompt engineering, adversarial robustness, and automated AI workflow orchestration. Experience collaborating with cross-functional teams in Agile and DevOps environments to deliver scalable, reliable, and production-grade AI systems. Passionate about building reliable, interpretable, and scalable AI systems with focus on performance optimization, operational excellence, and responsible AI development.
Directed the end-to-end architecture of an enterprise-grade Retrieval-Augmented Generation (RAG) platform using Python and advanced ML methodologies.
Designed and implemented scalable ML pipelines for data ingestion, model training, validation, deployment, and monitoring across enterprise AI applications.
Built production-grade AI/ML services using Python, FastAPI, AWS SageMaker, ECS, and Kubernetes-based deployment architectures.
Improved inference performance and reduced operational costs through model optimization, caching strategies, and low-latency API integrations.
Developed scalable FastAPI-based backend services supporting enterprise Generative AI and real-time intelligent chat applications.
Built LangGraph and LangChain agent workflows for multi-step reasoning, orchestration, and AI-driven automation processes.
Developed automated evaluation pipelines for LLM responses, improving reliability, safety validation, and model quality assessment across enterprise AI applications.
Worked on scalable inference architectures supporting low-latency AI workloads, asynchronous request processing, and high-throughput distributed systems.
Participated in AI governance and responsible AI initiatives including secure deployment practices, model monitoring, and operational risk mitigation.
Assisted in implementing observability and monitoring frameworks for production AI systems including performance metrics, alerting, and inference tracking.
Built and supported production-grade Amazon EKS environments for containerized workloads with focus on scalability, availability, and operational stability.
Implemented Kubernetes deployment configurations using Helm charts for application packaging, release management, and environment standardization.
Worked on autoscaling implementations for Kubernetes workloads using KEDA and event-driven scaling strategies to optimize resource utilization.
Directed the end-to-end architecture of an enterprise-grade Retrieval-Augmented Generation (RAG) platform using Python and advanced ML methodologies., Designed and implemented scalable ML pipelines for data ingestion, model training, validation, deployment, and monitoring across enterprise AI applications., Built production-grade AI/ML services using Python, FastAPI, AWS SageMaker, ECS, and Kubernetes-based deployment architectures., Improved inference performance and reduced operational costs through model optimization, caching strategies, and low-latency API integrations., Developed scalable FastAPI-based backend services supporting enterprise Generative AI and real-time intelligent chat applications., Built LangGraph and LangChain agent workflows for multi-step reasoning, orchestration, and AI-driven automation processes., Developed automated evaluation pipelines for LLM responses, improving reliability, safety validation, and model quality assessment across enterprise AI applications., Worked on scalable inference architectures supporting low-latency AI workloads, asynchronous request processing, and high-throughput distributed systems., Participated in AI governance and responsible AI initiatives including secure deployment practices, model monitoring, and operational risk mitigation., Assisted in implementing observability and monitoring frameworks for production AI systems including performance metrics, alerting, and inference tracking., Built and supported production-grade Amazon EKS environments for containerized workloads with focus on scalability, availability, and operational stability., Implemented Kubernetes deployment configurations using Helm charts for application packaging, release management, and environment standardization., Worked on autoscaling implementations for Kubernetes workloads using KEDA and event-driven scaling strategies to optimize resource utilization.
Developed data-driven features integrated into applications, improving user-facing analytics and personalization., Gained experience working with Linux-based systems for data processing and model deployment., Applied algorithmic thinking and data structures to improve model efficiency and processing speed., Developed machine learning models and analytics solutions using Python, Pandas, NumPy, and Scikit-learn for enterprise banking applications., Built scalable backend APIs and ML-enabled services supporting intelligent automation and analytics workflows., Assisted in Kubernetes cluster administration activities including deployment support, monitoring, and workload management., Supported infrastructure provisioning and cloud deployment activities using Terraform and AWS services., Supported ML experimentation workflows including feature engineering, model evaluation, hyperparameter tuning, and performance analysis for predictive applications., Worked on scalable data pipelines and distributed processing workflows supporting enterprise analytics and intelligent automation systems., Worked with Docker, Kubernetes, and CI/CD pipelines supporting cloud-native application deployments., Participated in AWS networking implementations involving VPCs, routing configurations, Security Groups, and load balancers.
Developed data-driven features integrated into applications, improving user-facing analytics and personalization.
Gained experience working with Linux-based systems for data processing and model deployment.
Applied algorithmic thinking and data structures to improve model efficiency and processing speed.
Developed machine learning models and analytics solutions using Python, Pandas, NumPy, and Scikit-learn for enterprise banking applications.
Built scalable backend APIs and ML-enabled services supporting intelligent automation and analytics workflows.
Assisted in Kubernetes cluster administration activities including deployment support, monitoring, and workload management.
Supported infrastructure provisioning and cloud deployment activities using Terraform and AWS services.
Supported ML experimentation workflows including feature engineering, model evaluation, hyperparameter tuning, and performance analysis for predictive applications.
Worked on scalable data pipelines and distributed processing workflows supporting enterprise analytics and intelligent automation systems.
Worked with Docker, Kubernetes, and CI/CD pipelines supporting cloud-native application deployments.
Participated in AWS networking implementations involving VPCs, routing configurations, Security Groups, and load balancers.
Contributed to observability improvements using Prometheus, Grafana, Datadog, and CloudWatch dashboards.
Worked on scalable AI platform components supporting model deployment, inference workflows, and production monitoring.
Assisted in building resilient microservices and distributed backend systems for enterprise AI applications.
Supported CI/CD automation and containerized deployment workflows using Docker and Kubernetes.
Worked with real-time processing systems and low-latency AI service integrations.
Participated in architecture reviews, code reviews, and technical discussions focused on scalability and operational excellence.
Contributed to enterprise API design, backend integrations, and cloud-native application development practices.
Directed the end-to-end architecture of an enterprise-grade Retrieval-Augmented Generation(RAG) platform using Python and advanced ML methodologies. Engineered scalable ingestion pipelines with OCR, semantic chunking, and embedding generation. Designed hybrid retrieval systems (BM25 + FAISS + reranking). Integrated OpenAI GPT-4 APIs with LangChain pipelines. Built interactive AI-powered user experiences enabling natural language querying and documentexploration for non-technical users. Worked in Linux-based environments for development, debugging, and deployment of scalableapplications. Led design decisions for scalable GenAI architecture, ensuring high availability and low latency forenterprise users. Designed agent-based workflows for intelligent document querying and contextual reasoning. Implemented vector search optimization techniques improving retrieval accuracy and performance. Built streaming AI pipelines using Kafka, enabling real-time inference and data processing. Developed REST and WebSocket APIs for real-time AI interactions. Contributed to LLM evaluation frameworks, improving response quality and reducinghallucinations. Optimized infrastructure for cost-efficient LLM deployment using batching and caching strategies. Applied multithreading and parallel processing concepts to optimize real-time data pipelines andreduce latency. Developed LLM-backed APIs consumed by frontend applications to deliver real-time intelligentresponses. Collaborated with product managers and UX designers to prototype
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