[Job-31573] AI Engineer Master

ciandt; United States; Hybrid; USD 180,000 - 270,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Life insurance

Meal allowance

Childcare support

Paternity leave

Profit sharing

Wellness program

Learning platform

Discount club

Accessibility support

Job summary

CIANDT is seeking a senior AI architect to lead the design and deployment of production-grade AI solutions for enterprise clients and internal teams. You will shape architecture across generation, retrieval, and deployment, with emphasis on LLMOps, RAG, and multi-agent systems.

You will combine hands-on engineering with technical leadership, drive measurable business impact, and collaborate across global teams using Python and modern AI tooling such as LangChain, vector databases, and cloud

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, Information Systems, or a related field.
• Advanced English proficiency for global collaboration.
• Proven experience developing and deploying Generative AI solutions in production.
• Background designing technical architectures for complex, business-critical enterprise systems.
• Demonstrated technical leadership across multidisciplinary initiatives.
• Advanced proficiency in Python.

Responsibilities

• Architect AI solutions balancing performance, scalability, security, governance, cost, and long-term sustainability.
• Build AI-powered products and platforms that meet enterprise engineering standards and business objectives.
• Design intelligent and multi-agent systems covering planning, reasoning, task orchestration, and tool usage.
• Develop advanced RAG architectures using embeddings, vector databases, GraphRAG, hybrid search, reranking, and grounding.
• Integrate AI capabilities with enterprise ecosystems such as ERPs, CRMs, data platforms, and transactional systems.
• Evaluate large language models, small language models, and multimodal components, defining quality and evaluation metrics.
• Implement observability and monitoring across performance, cost, latency, usage, and model behavior.
• Establish LLMOps pipelines covering development, testing, versioning, deployment, and ongoing monitoring.
• Act as a technical reference, shaping architectural decisions and promoting engineering best practices.

Skills

Python

LangChain

LangGraph

Semantic Kernel

LlamaIndex

OpenAI / Azure OpenAI

RAG architectures

Vector databases

Education

Bachelor's degree in CS/CE/SE/IS

Tools

Azure

AWS

GCP

APIs / microservices

Job description

Role overview

This is a senior technical role within a Data Science and AI practice, designing and delivering production-grade artificial intelligence solutions for enterprise clients and internal teams. The work spans architecture through operations, with emphasis on Generative AI, agentic systems, Retrieval-Augmented Generation, and LLMOps. The position suits an engineer who combines deep hands-on expertise with technical leadership and a focus on measurable business impact.

Responsibilities

• Architect AI solutions balancing performance, scalability, security, governance, cost, and long-term sustainability.
• Build AI-powered products and platforms that meet enterprise engineering standards and business objectives.
• Design intelligent and multi-agent systems covering planning, reasoning, task orchestration, and tool usage.
• Develop advanced RAG architectures using embeddings, vector databases, GraphRAG, hybrid search, reranking, and grounding.
• Integrate AI capabilities with enterprise ecosystems such as ERPs, CRMs, data platforms, and transactional systems.
• Evaluate large language models, small language models, and multimodal components, defining quality and evaluation metrics.
• Implement observability and monitoring across performance, cost, latency, usage, and model behavior.
• Establish LLMOps pipelines covering development, testing, versioning, deployment, and ongoing monitoring.
• Act as a technical reference, shaping architectural decisions and promoting engineering best practices.

Requirements

• Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, Information Systems, or a related field.
• Advanced English proficiency for global collaboration.
• Proven experience developing and deploying Generative AI solutions in production.
• Background designing technical architectures for complex, business-critical enterprise systems.
• Demonstrated technical leadership across multidisciplinary initiatives.
• Advanced proficiency in Python.
• Hands-on experience with LangChain, LangGraph, Semantic Kernel, LlamaIndex, or comparable frameworks.
• Familiarity with OpenAI, Azure OpenAI, Anthropic, Gemini, or similar AI platforms.
• Deep knowledge of RAG architectures, embeddings, and semantic search.
• Experience with vector databases such as Pinecone, Weaviate, Qdrant, or Milvus.
• Experience building APIs and microservices on Azure, AWS, or GCP.
• Knowledge of observability, monitoring, and reliability engineering practices.

Nice to have

• Experience building AI platforms used across multiple clients or business units.
• Production experience implementing multi-agent architectures.
• Familiarity with fine-tuning, PEFT, LoRA, and other model customization techniques.
• Knowledge of Knowledge Graphs and GraphRAG.
• Experience with automated LLM evaluation frameworks.
• Advanced MLOps and LLMOps knowledge.
• Experience with Databricks or Snowflake.
• Open-source contributions, technical community involvement, or AI-related publications.
• Advanced certifications in AI, cloud architecture, or software engineering.

Benefits and work setup

• Health and dental insurance, plus life insurance.
• Meal and food allowance.
• Childcare assistance and extended paternity leave.
• Profit Sharing and Results Participation program.
• Gym and wellness partnerships through major well-being platforms.
• Continuous learning platform plus partnerships with online and language learning providers.
• Discount club and dedicated physical and mental well-being resources.
• Inclusive hiring with accommodations and accessibility support throughout the selection process.