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Principal AI Engineer designs and owns the shared AI architecture for multi-agent marketing systems, retrieval pipelines, and evaluation frameworks that power SMB-focused products at scale.
Own AI agent-based products for B2SMB marketing, writing specs, building prototypes, and iterating on prompts and retrieval to ship non-deterministic features.
Fine-tune and adapt large language models to improve instruction-following and domain-specific performance for production use.
Fine-tunes large language models using instruction fine-tuning and domain adaptation to improve model relevance and performance for specific contexts.
Architect and implement generative AI solutions on SAP BTP, designing RAG pipelines, knowledge graphs, and LangChain orchestration to embed LLMs into enterprise SAP applications.
Location: Bangalore - Indraprastha, India Thales is a global technology leader trusted by governments, institutions, and enterprises to tackle their most demanding challenges. From quantum applications and artificial…
Builds and deploys production-grade agentic AI systems (LLMs, RAG, orchestration) for American Express’s global commercial services, integrating with cloud-native stacks (Go/TypeScript/Python, AWS/GCP, Kafka, Kubernetes) to enhance customer-facing financial solutions.
Build and deploy ML models for business decisions and optimize AI agentic systems like conversational tools, using Python, SQL, and libraries such as PyTorch.
Lead the architecture and delivery of ML and GenAI platforms for JPMorganChase’s Home Lending, turning prototypes into production systems and mentoring AI engineers.
Design and deploy enterprise AI solutions on Red Hat OpenShift AI, including GenAI, agentic AI, and MLOps workflows, while advising customers and leading project teams.
Build and maintain production-grade LLM evaluation frameworks and pipelines for a legal-focused AI analytics platform, continuously benchmarking models and optimizing document understanding and summarization workflows.
Design and deploy AI solutions with agentic workflows and RAG pipelines using Python, LLMs, and cloud architectures in a regulated environment.
Design and deploy cutting-edge AI/ML applications, including LLMs and computer vision, in a defense-focused R&D environment requiring Secret clearance.
Build autonomous AI agents using LangChain, LangGraph, and AutoGen, integrating RAG, knowledge graphs, and fine-tuned SLMs for edge deployment.
Builds and deploys LLM-powered agentic workflows for network troubleshooting using LangGraph, MCP servers, and Kubernetes, focusing on reasoning pipelines, tool integration, and production-scale AI systems.
Lead the machine-learning systems that rank, recommend, and retrieve restaurants and menu items for millions of diners, using deep learning, embeddings, and LLMs to optimize conversion and long-term value.
Build production-grade AI systems (RAG, agents, APIs) for logistics operations, focusing on retrieval, evaluation, and cost-efficient deployment while owning end-to-end delivery.
Build evaluation and benchmarking infrastructure for an agentic AI platform at a cybersecurity company, using Python, LLM evaluation frameworks, knowledge graphs, and vector databases.
About Scale AI Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with leading enterprises and government organizations to accelerate their AI…
Canonical is a leading provider of open source software and operating systems to the global enterprise and technology markets. Our platform, Ubuntu, is very widely used in breakthrough enterprise initiatives such as…
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