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Lead the design and implementation of scalable GenAI, LLM, and AI-agent architectures, including RAG and MLOps/LLMOps pipelines, to deliver enterprise solutions and accelerate client adoption.
Lead the design and deployment of advanced LLM-driven agentic AI systems for enterprise workflows, setting technical standards and mentoring teams to build robust, autonomous AI co-workers at scale.
Builds and deploys production-grade AI/data pipelines: LLM apps (RAG, agents), graph/ETL workflows, and cloud-native data platforms using Python, FastAPI, Airflow, Snowflake, and vector stores.
Build and deploy LLM-based AI systems for government services using RAG, fine-tuning, and prompt engineering with Python, LangChain, and cloud ML services.
Design and lead cloud-native AI architectures, integrating agentic systems with tools like LangChain and RAG to build scalable enterprise platforms.
Design and build production-grade AI/ML platforms and MLOps pipelines for clients, using Python, cloud (GCP/AWS/Azure), Terraform, and tools like Vertex AI and Kubernetes.
Build and operate an internal AI platform for a fintech company, designing retrieval layers, agent runtimes, and deployment pipelines using Python, FastAPI, and cloud services.
Build autonomous AI agents and automation pipelines for an FMCG enterprise, integrating LLMs with enterprise systems using RAG and Azure AI tools to handle pricing, sales, and operations workflows.
Build and deploy AI-powered applications using LLMs, vector databases, and AI orchestration frameworks like LangChain or Semantic Kernel.
Build and maintain AI Agent applications using LLMs for intelligent Q&A, task planning, and workflow automation in energy-storage systems.
Build AI-ready data pipelines and knowledge systems for an investment firm’s agentic AI, integrating structured financial data, unstructured research, and real-time feeds into vector stores, graph databases, and retrieval pipelines.
Backend Engineer building scalable AI orchestration frameworks, RAG systems, and cloud-native AI services using Python, PostgreSQL, and vector databases to power enterprise-grade generative AI applications.
Build full-stack web apps and integrate AI features using modern stacks (React, Node.js/Python/.NET, SQL/NoSQL) while leveraging AI coding assistants daily.
Build and deploy AI-powered web apps and APIs for a digital asset platform, integrating LLMs, RAG, and agentic frameworks with Java Spring Boot, Kubernetes, and AWS.
Build full-stack web apps and integrate AI features using modern stacks, AI coding assistants, and LLM APIs to deliver enterprise solutions.
Build and maintain SGInnovate’s Deep Tech Central platform using full-stack skills (Next.js, NestJS, PHP, Drupal) and integrate AI features with AWS Lambda, vector databases, and LLM APIs.
Build and deploy ML models (churn, pricing, fraud) and LLM-based agents on AWS SageMaker to automate insurance workflows and support data-driven decisions across the business.
Design and deploy enterprise-scale AI systems for banking, integrating LLMs and ML platforms while ensuring regulatory compliance and high availability.
Build and deploy AI solutions for enterprise customers, including predictive models, GenAI chatbots, and agentic AI systems using Python, LangGraph, and Kubernetes.
Build production-grade AI systems like copilots, RAG, and agents using LLMs and vector databases, deploying them in enterprise environments.
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