AI/ML Engineer

Programmers.io; Minnesota; On-site; USD 140,000 - 210,000; Full time

Job summary

Programmers.io is seeking a Senior AI/ML Engineer to design, build, and scale enterprise-grade AI solutions powered by GenAI, RAG, and advanced integration architectures. This role focuses on intelligent copilots, AI agents, agent orchestration, and knowledge retrieval across cloud and business platforms.

The ideal candidate combines strong ML expertise with deep software engineering and systems integration to deliver production-ready AI capabilities.

Qualifications

• Senior AI/ML Engineer with enterprise-scale ML experience.
• Experience with GenAI, LLMs, RAG, and cloud integrations.

Responsibilities

• Design, develop, and deploy scalable AI/ML and Generative AI solutions using cloud-native architectures and modern engineering practices.
• Build production-grade GenAI applications leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, semantic search, and agentic workflows.
• Implement advanced retrieval strategies including dynamic context retrieval, query rewriting, cross-encoder reranking, Graph RAG, and hybrid embedding architectures.
• Design and implement multi-agent systems and orchestration frameworks using LangGraph, Semantic Kernel, AutoGen, CrewAI, MCP, or equivalent technologies.
• Develop intelligent routing and classification systems using LLMs and traditional ML models (BERT/classification models) for domain detection, query complexity assessment, and workflow orchestration.
• Fine-tune and adapt foundation models using supervised fine-tuning (SFT), prompt engineering, retrieval optimization, and domain-specific training datasets.
• Build domain-aware retrieval systems including localized content retrieval, metadata-driven filtering, chunking strategies, and knowledge graph integrations.
• Develop enterprise integrations with APIs, databases, messaging platforms, document repositories, and internal business systems.
• Optimize AI systems for latency, scalability, reliability, cost efficiency, accuracy, and user experience.

Skills

GenAI & LLMs

RAG & retrieval

Cloud-native

Multi-agent systems

Job description

Programmers.io is currently looking for an \"AI/ML Engineer\"

Onsite in Minneapolis, MN, United States

.

Contract Role (Open for C2C OR W2)

Job Summary-

We are seeking a highly skilled Senior AI/ML Engineer to design, build, and scale enterprise-grade AI solutions powered by Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), and advanced integration architectures. The ideal candidate combines strong machine learning expertise with deep software engineering and systems integration experience to deliver production-ready AI capabilities that solve complex business problems at scale.

This role will focus on building intelligent copilots, AI agents, agent orchestration frameworks, knowledge retrieval systems, evaluation frameworks, and enterprise integrations across cloud and business platforms.

Key Responsibilities-

• Design, develop, and deploy scalable AI/ML and Generative AI solutions using cloud-native architectures and modern engineering practices.
• Build production-grade GenAI applications leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, semantic search, and agentic workflows.
• Implement advanced retrieval strategies including dynamic context retrieval, query rewriting, cross-encoder reranking, Graph RAG, and hybrid embedding architectures.
• Design and implement multi-agent systems and orchestration frameworks using LangGraph, Semantic Kernel, AutoGen, CrewAI, MCP, or equivalent technologies.
• Develop intelligent routing and classification systems using LLMs and traditional ML models (BERT/classification models) for domain detection, query complexity assessment, and workflow orchestration.
• Fine-tune and adapt foundation models using supervised fine-tuning (SFT), prompt engineering, retrieval optimization, and domain-specific training datasets.
• Build domain-aware retrieval systems including localized content retrieval, metadata-driven filtering, chunking strategies, and knowledge graph integrations.
• Develop enterprise integrations with APIs, databases, messaging platforms, document repositories, and internal business systems.
• Optimize AI systems for latency, scalability, reliability, cost efficiency, accuracy, and user experience.