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DevOps Engineer (AI / LLM / Kubernetes)
Builds and scales Kubernetes-based AI infrastructure for a legal LLM platform, handling GPU workloads, CI/CD pipelines, and high-load monitoring to support real-time inference and integrations.
Algotale(Unilog)-Product Engineer — Support
Job Title: Product Engineer — Support (CX1 Platform) Location: Bangalore / Mysore Reports to: Engineering Support Manager Positions: 3 Job Synopsis: The Product Engineer — Support owns the stability and resolution of a…
Data Engineer
Designs and maintains multi-cloud data pipelines between AWS and GCP, builds large-scale data processing systems, and ensures data quality and security. Core technologies include Python, PySpark, SQL, Apache Airflow, dbt, Redshift, BigQuery, and S3.
Forward Deployed Engineer (FDE)
A Forward Deployed Engineer (FDE) at Mentat deploys an enterprise AI platform on-premise at client sites, configures GPU clusters, integrates with customer systems, solves on-site problems, and provides feedback to improve the product, focusing on LLMs, embeddings, and containerized infrastructure.
Senior Data Platform Engineer
Senior Data Platform Engineer at DigiCert designing, building, and operating foundational data and ML infrastructure on Databricks, using Python, SQL, and Spark to power analytics and machine learning across the organization.
Forward Deployed Engineer
Build and deploy AI solutions (LLM agents, RAG pipelines) for clients, then guide teams through adoption to ensure measurable impact and lasting integration.
Java Developer Intern - Process Platform & Automation - TalentBank 2026
An internship developing Java-based applications for Commerzbank’s cloud-native process automation platform, focusing on microservices, automation, and incident support with Agile collaboration.
(Senior) Software Engineer - AI/ML (m/f/d)
Build and scale backend services in Java/Spring Boot while integrating AI tools to enhance engineering workflows and product capabilities for a fast-growing SaaS workforce management platform.
AI & Integration Engineer (m/f/d) - Member of Technical Staff
Build and operate production-grade AI workflows and data pipelines using Python and enterprise platforms like Langdock and Microsoft Copilot to integrate AI capabilities into internal systems.
(Senior) Software Engineer – AI/ML (m/f/d)
Build and operate scalable backend services that power AI-driven workforce management features using Java, Python, and cloud-native tools.
Senior Software Engineer (Python + Rust)
Builds and maintains high-performance backend services in Rust and Python (FastAPI) within a microservices architecture to power an edtech platform.
Senior Software Engineer (Python, Rust)
Build and maintain high-performance backend services in Rust and Python (FastAPI) for an edtech platform, designing microservices and improving system reliability.
ML Ops Engineer
Builds and maintains MLOps infrastructure to deploy, monitor, and scale AI models in production for an edtech platform, using MLflow, Kubernetes, and CI/CD pipelines.
Backend developer
Develops a backend service in Quarkus/Java 21 to route and secure API calls (REST/MCP) to external/internal tools (weather, currency, banking ops), handling auth, streaming responses, and circuit breakers. Works with BAs, writes tests, and refactors code with CI/CD tools.
Аналитик данных (Риски)
Разработка мультиагентной AI-системы для раннего выявления рисков в сегменте микробизнеса. Сбор данных, обучение LLM-агентов, построение прогнозных моделей и интеграция решений с финансовыми департаментами.
DevSecOps в Кибербезопасность
DevSecOps engineer building and evolving an internal automated code and artifact scanning platform, integrating AI/LLM-based scanning, and advising dev teams on security practices and vulnerability remediation.
Staff Engineer, Generative AI Engineer
Design and deploy enterprise-grade Generative AI solutions, including AI agents, RAG pipelines, and multi-agent architectures using Python, FastAPI, and modern AI frameworks.
AI Engineer (ИИ-сценарии)
Builds production-grade AI services by integrating LLMs, RAG pipelines, and tool calling into end-to-end workflows (e.g., travel booking, car selection, education matching) while ensuring quality, observability, and scalability.