AI Application Engineer Job at Rearc

Job Description

As an AI Application Engineer at Rearc, you'll contribute to the technical excellence of our machine learning and artificial intelligence capabilities. Your expertise in Machine Learning, AI application development, and LLMOps will help design and deploy intelligent systems that are efficient, reliable, and production-ready. You'll work closely with cross-functional teams to build and operationalize GenAI solutions, pushing the boundaries of what AI is capable of in customer environments. Building strong partnerships with technical teams & stakeholders and aligning with business objectives will be essential as you support AI-driven initiatives and contribute to their successful execution.

Job Responsibility

Collaborate with Colleagues – Work closely with colleagues to understand customers' business objectives and technical challenges, contributing to the design and development of effective GenAI solutions tailored to client needs
Apply GenAI Principles – Utilize modern tools and frameworks like LangGraph, to build scalable, reliable, and maintainable Compound AI systems. Leverage your understanding of AI fundamentals to ensure every project meets rigorous industry and ethical standards
Adapt to the latest Technologies & Patterns – continue to research, learn, and stay abreast of the most recent state of the art for AI application development
Promote Knowledge Sharing –Bolster our culture of continuous learning by sharing knowledge about AI engineering best practices through blog posts, articles, and internal talks. Support a collaborative environment that fosters shared expertise and ongoing innovation across our community

Requirements

2+ years of experience in AI engineering, machine learning (ML), or related fields
Strong understanding of state of the art techniques in generative AI, including large language models (LLMs), text generation and other foundation models
Familiarity with AI orchestration tools (e.g. LangGraph, CrewAI, Bedrock Agents, smolagents, etc)
Experience in fine-tuning, prompt engineering or otherwise adapting generative models for specific use cases
Experience with AI model evaluation, including human-in-the-loop and LLM judge paradigms
Familiarity with NLP libraries and frameworks
Hands-on experience in implementing Retrieval Augmented Generation (RAG) architectures and integrating retrieval systems with generative models
Knowledge of at least one vector store or database (e.g. Opensearch, Pinecone, PostgreSQL with pgvector) and techniques for similarity search
Familiarity with common data ingestion/ETL patterns for populating knowledge bases
Experience with implementing LLM tool calling (either directly, via an orchestration framework, or using Model Context Protocol (MCP) clients)
Experience using Amazon Bedrock or Databricks Mosaic AI for deploying and managing generative AI models
Ability to integrate Generative AI scripts with other services (such as AWS S3 or Lambda or Databricks Apps) to build scalable and secure AI solutions
Strong programming skills in Python (or similar languages)
Familiarity with CI/CD pipelines and MLOps practices to ensure seamless integration, testing and deployment of AI models

Nice to have

Experience with containerization and orchestration for deploying AI services
Understanding of cloud security best practices and cloud architecture design in the context of AI workloads
Experience writing MCP servers
Experience training or fine-tuning ML models (classical ML or LLMs/diffusion models)

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AI Application Engineer

Job Description

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