AI Engineer – AWS Bedrock, Claude & Databricks

VeeAR Projects Inc.; Raleigh (NC); On-site; USD 120,000 - 150,000; Full time

Job summary

VeeAR Projects Inc. is seeking a highly skilled AI Engineer to lead the design and deployment of enterprise-scale AI solutions. The ideal candidate will have expertise in Generative AI, AWS cloud technologies, and Databricks platforms.

Responsibilities include developing AI capabilities, optimizing workflows, and collaborating with various teams to ensure secure AI operations in a regulated environment.

Qualifications include a degree in a relevant field and significant experience in software and AI engineering.

Qualifications

• 8+ years of experience in software engineering, AI engineering, machine learning, or cloud data platforms.
• Strong hands-on experience with AWS Bedrock and Anthropic Claude models.
• Experience building GenAI applications for conversational AI, intelligent document processing, summarization.

Responsibilities

• Design, develop, and deploy enterprise Generative AI solutions leveraging AWS Bedrock.
• Build APIs and microservices to operationalize AI capabilities across applications.
• Collaborate with teams to ensure compliant AI adoption aligned with standards.

Skills

AWS Bedrock

Generative AI

Anthropic Claude models

Data engineering

Python

MLflow

Databricks Lakehouse

MLOps

Education

Bachelor's or master's degree in computer science, Data Science, Engineering, or related discipline

Tools

Databricks

Apache Spark

Vector Databases

REST APIs

AWS Lambda

Job description

We are seeking a highly skilled and hands-on AI Engineer with deep expertise in Generative AI, AWS cloud technologies, Anthropic Claude models, and Databricks-based data engineering and analytics platforms. This role will lead the design, development, and deployment of enterprise-scale AI and machine learning solutions supporting research, clinical, commercial, and operational initiatives across the organization.

The ideal candidate will possess strong experience building production-ready AI solutions using AWS Bedrock, Anthropic Claude LLMs, vector databases, Retrieval-Augmented Generation (RAG) architectures, and Databricks Lakehouse technologies. The role requires close collaboration with data scientists, data engineers, cybersecurity teams, and business stakeholders to operationalize scalable, secure, and compliant AI capabilities in a regulated healthcare and life sciences environment.

Key Responsibilities

• Design, develop, and deploy enterprise Generative AI solutions leveraging AWS Bedrock and Anthropic Claude models.
• Build advanced conversational AI, summarization, document intelligence, and knowledge retrieval solutions using Claude APIs and Bedrock-hosted foundation models.
• Develop and optimize Retrieval-Augmented Generation (RAG) architectures using embeddings, vector databases, semantic search, and prompt orchestration frameworks.
• Engineer scalable AI and ML workflows within Databricks using Delta Lake, Apache Spark, MLflow, and Lakehouse architectures.
• Develop prompt engineering strategies, AI guardrails, context management, and response evaluation frameworks for enterprise AI applications.
• Integrate structured and unstructured clinical, research, regulatory, and commercial datasets into AI-driven solutions.
• Build APIs and microservices to operationalize AI capabilities across enterprise applications and workflows.
• Implement MLOps and LLMOps best practices for model deployment, monitoring, governance, observability, and lifecycle management.
• Collaborate with cybersecurity and governance teams to ensure secure, compliant, and responsible AI adoption aligned with HIPAA, GxP, and enterprise data governance standards.
• Optimize AI workloads for performance, scalability, resiliency, and cost efficiency within AWS cloud environments.
• Contribute to enterprise AI architecture standards, reusable AI frameworks, and innovation initiatives.

Required Qualifications

• Bachelor's or master's degree in computer science, Data Science, Engineering, or related discipline.
• 8+ years of experience in software engineering, AI engineering, machine learning, or cloud data platforms.
• Strong hands-on experience with AWS Bedrock and Anthropic Claude models in enterprise environments.
• Experience building GenAI applications using Claude for conversational AI, intelligent document processing, summarization, and enterprise search use cases.
• Expertise with prompt engineering, prompt chaining, context window optimization, and RAG implementation patterns.
• Experience with Databricks Lakehouse Platform, Delta Lake, Apache Spark, and MLflow.
• Strong programming skills in Python and experience with LangChain, LlamaIndex, or similar AI orchestration frameworks.
• Experience integrating vector databases and semantic search technologies.
• Strong understanding of AWS-native cloud architectures and AI services.
• Experience with REST APIs, microservices, CI/CD pipelines, containerization, and infrastructure automation.
• Familiarity with AI governance, security controls, and responsible AI practices.
• Experience working in regulated industries such as healthcare, life sciences, or pharmaceuticals preferred.

Preferred Skills

• Experience with clinical, genomics, pharmaceutical, or regulatory datasets.
• Knowledge of AI agent frameworks and autonomous workflow orchestration.
• Experience implementing secure enterprise AI solutions with guardrails and model evaluation frameworks.
• Familiarity with Snowflake, Redshift, or cloud data modernization initiatives.
• AWS Certifications and/or Databricks certifications preferred.

Technical Environment

• AWS Bedrock
• Anthropic Claude
• Databricks Lakehouse Platform
• Python / PySpark
• MLflow
• Vector Databases
• AWS Lambda, ECS, EKS, S3, SageMaker
• REST APIs & Microservices

Success Criteria

• Deliver secure and scalable enterprise AI solutions leveraging Claude and AWS Bedrock.
• Accelerate adoption of Generative AI capabilities across research, clinical, and business operations.
• Establish reusable AI engineering frameworks and LLMOps standards.
• Improve data accessibility, operational efficiency, and AI governance maturity across the enterprise.