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Senior AI/ML Engineer (LLM / GenAI / NLP)
Builds and deploys LLM/NLP-based AI agents, RAG systems, and GenAI pipelines using Python, FastAPI, and PyTorch for enterprise clients.
Staff AI Platform Engineer, Infrastructure Services
Build and scale SentinelOne’s AI Gateway infrastructure (Kong-based) to route, secure, and monitor AI coding assistant traffic, while operating self-hosted LLM stacks and driving reliability across Kubernetes and CI/CD systems.
Senior AI Platform Engineer, Infrastructure Services
Build and scale SentinelOne’s AI Gateway infrastructure (Kong-based) to route, secure, and monitor AI coding assistant traffic, while operating self-hosted LLM stacks and driving reliability across Kubernetes and CI/CD tooling.
Senior Backend Engineer: Machine Learning Infrastructure
Build and scale backend services for Constructor’s AI-powered e-commerce search platform, focusing on ML infrastructure, model serving, and distributed systems.
Lead Data Engineer
Lead a team to design and build scalable data pipelines and distributed platforms using Python, PostgreSQL, Spark, Kafka, and vector databases.
Lead AI Engineer (команда Конструктор Комплексных Решений)
Designs and builds an AI system for dynamically assembling personalized user interaction workflows using modular AI components, focusing on architecture, integration, and R&D for Sber’s ecosystem.
Системный архитектор
Designs and builds the backend of an AI agent platform in Python, including orchestration, realtime APIs, vector search, RAG, and Kubernetes deployment.
Developer GenAI
Build and optimize Generative AI solutions using LLMs, RAG pipelines, and agent frameworks like LangChain to automate business processes and integrate with external systems.
AI Developer (LLM & GenAI)
Builds scalable AI solutions using LLMs (Claude, Gemini, ChatGPT) via prompt engineering, RAG pipelines, and agent orchestration, integrating them into business systems with Python and APIs.
Solutions Architect (AI)
Designs and implements AI/ML and generative AI solutions for enterprise clients, focusing on architecture, LLM/RAG pipelines, and MLOps—collaborating with data and product teams to scale AI from proof-of-concept to production while ensuring security, governance, and cloud platform integration.
Custom Software Engineer
Build AI-powered agentic applications using LLMs, RAG pipelines, and vector databases, then integrate them into enterprise systems with clean, testable code and CI/CD.
Custom Software Engineer
Build AI-powered agentic applications using LLMs, RAG pipelines, and vector databases; implement tools, prompts, and CI/CD while integrating enterprise APIs and ensuring safety.
Custom Software Engineer
Builds AI-native applications by implementing LLM tooling, RAG pipelines, and vector search; integrates AI into enterprise systems with Python/TypeScript/Java, frameworks like LangChain, and vector DBs (pgvector, Pinecone).
Custom Software Engineer
Build AI-powered agentic applications using LLMs, RAG pipelines, and vector databases, integrating enterprise APIs and ensuring robust testing and safety. Core stack includes Python, TypeScript/Node.js, and Java with frameworks like LangChain and Spring Boot.
Custom Software Engineer
Build agentic AI applications using LLMs, RAG pipelines, and vector search; implement tools, prompts, and CI/CD while integrating enterprise APIs and data sources.
Custom Software Engineer
Build AI-powered agentic applications using LLMs, RAG pipelines, and vector databases; implement tools, prompts, and CI/CD while integrating enterprise APIs and ensuring safety.
Custom Software Engineer
Build AI-powered agentic applications using LLMs, RAG pipelines, and vector databases. Develop, test, and integrate agents with enterprise APIs and cloud services.
Custom Software Engineer
Build AI-powered applications using LLMs, RAG pipelines, and vector databases. Develop agents, prompts, and integrations with clean code and CI/CD in Python, Java, or TypeScript.
Custom Software Engineer
Custom Software Engineer at Accenture in Chennai builds agentic AI applications using LLM tooling, RAG pipelines, vector search, and API integrations, with core techs including Java Full Stack, Python, TypeScript/Node.js, and vector DBs.