Gen AI Engineer — Enterprise AI & RAG Architect

Realign Llc; Plano (TX); On-site; USD 130,000 - 170,000; Full time

Job summary

Realign Llc is seeking a Gen AI engineer to design and implement AI applications using AWS Bedrock, build scalable workflows, and optimize prompt strategies. You will work with product owners, architects, data engineers, and security teams to ensure high-quality, secure, and cost-efficient AI solutions for enterprise-scale use cases.

The role requires hands-on experience with Python or Java, knowledge of foundation models, embedding, RAG, and vector search, and familiarity with MCP concepts and

Qualifications

• Design and implement Gen AI applications using AWS Bedrock and related AWS services.
• Build secure, scalable, and maintainable AI workflows for enterprise use cases.
• Develop retrieval-augmented generation solutions that use enterprise knowledge sources effectively.
• Manage context windows, memory strategies, prompt composition, and grounding techniques.
• Integrate MCP-based tools and services to extend model capabilities and connect to enterprise systems.
• Collaborate with product owners, architects, data engineers, and security teams.
• Evaluate model outputs for quality, safety, relevance, and factual accuracy.
• Optimize latency, cost, and token usage across AI workloads.
• Create reusable prompts, agent patterns, and orchestration components.
• Support deployment, monitoring, logging, and continuous improvement of Gen AI systems.
• Strong experience with Python or Java.
• Hands-on experience with AWS Bedrock.
• Understanding of foundation models, embeddings, RAG, and vector search.
• Experience with prompt engineering and context management.
• Familiarity with MCP concepts and tool integration patterns.
• Experience with APIs, microservices, and cloud-native architecture.
• Knowledge of enterprise security, privacy, and governance requirements.
• Ability to evaluate model performance and improve response quality.
• Good communication and stakeholder collaboration skills.

Responsibilities

• Design and implement Gen AI applications using AWS Bedrock and related AWS services.
• Build secure, scalable, and maintainable AI workflows for enterprise use cases.
• Develop retrieval-augmented generation solutions that use enterprise knowledge sources effectively.
• Manage context windows, memory strategies, prompt composition, and grounding techniques.
• Integrate MCP-based tools and services to extend model capabilities and connect to enterprise systems.
• Collaborate with product owners, architects, data engineers, and security teams.
• Evaluate model outputs for quality, safety, relevance, and factual accuracy.
• Optimize latency, cost, and token usage across AI workloads.
• Create reusable prompts, agent patterns, and orchestration components.
• Support deployment, monitoring, logging, and continuous improvement of Gen AI systems.
• Strong experience with Python or Java.
• Hands-on experience with AWS Bedrock.
• Understanding of foundation models, embeddings, RAG, and vector search.
• Experience with prompt engineering and context management.
• Familiarity with MCP concepts and tool integration patterns.
• Experience with APIs, microservices, and cloud-native architecture.
• Knowledge of enterprise security, privacy, and governance requirements.
• Ability to evaluate model performance and improve response quality.
• Good communication and stakeholder collaboration skills.

Skills

Gen AI workflows

AWS Bedrock

Python

Prompt engineering

Tool integration

Security governance

LangChain

Tools

OpenSearch

Aurora PostgreSQL

DynamoDB

KMS

CloudWatch

Lambda

Job description

Realign Llc is seeking a Gen AI engineer to design and implement AI applications using AWS Bedrock, build scalable workflows, and optimize prompt strategies. You will work with product owners, architects, data engineers, and security teams to ensure high-quality, secure, and cost-efficient AI solutions for enterprise-scale use cases.

The role requires hands-on experience with Python or Java, knowledge of foundation models, embedding, RAG, and vector search, and familiarity with MCP concepts and