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# Senior AI Platform Engineer

- **Company:** Alten Group
- **Location:** Warsaw, IN, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Continuous Integration, Elasticsearch, Information Extraction, Information Retrieval, Python (Programming Language), PostgreSQL, Machine Learning, Azure Machine Learning, Search Technologies, Software Engineering, SQL Databases, Enterprise Search, QLoRA, LangChain, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Agentic-AI, AI Platforms, Kubernetes, AutoGen, Machine Learning Operations, Human in the Loop, Docker
- **Published:** October 22, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pthn7wqa8p

## About the Role

* 4+ years of commercial experience in Software Engineering, Machine Learning Engineering, or AI Engineering
 * Strong Python development skills and experience delivering production AI applications
 * Hands-on experience with LLMs, Agentic AI, RAG, and AI orchestration frameworks (e.g., LangGraph, AutoGen, LangChain, Google ADK, or similar)
 * Experience building and evaluating AI systems using metrics, testing frameworks, and human feedback loops
 * Solid understanding of fine-tuning techniques such as SFT, LoRA, QLoRA, or preference-based optimization
 * Knowledge of Elasticsearch, vector databases, SQL/PostgreSQL, and enterprise search solutions
 * Practical experience with Docker, Kubernetes, and cloud platforms (AWS, Azure, or GCP)
 * Familiarity with distributed workloads, MLOps practices, CI/CD pipelines, and production monitoring
 * Fluent English - at least B2+/C1

## Description

* Design, build, and operate AI/ML services powering single- and multi-agent workflows
 * Develop production-grade GenAI solutions using agent frameworks, tool calling, memory, orchestration, and human-in-the-loop capabilities
 * Build and optimize RAG, search, and reasoning systems using Elasticsearch, vector search, and knowledge retrieval techniques
 * Create document intelligence pipelines for OCR, information extraction, classification, summarization, and embedding generation
 * Adapt and optimize AI models through fine-tuning approaches such as SFT, LoRA, and QLoRA
 * Deploy and scale AI workloads on Kubernetes and cloud platforms (AWS, Azure, or GCP)
 * Improve reliability, quality, observability, security, and performance of AI services in production environments