AI Solutions Engineer

Job Description

We are seeking an AI Solutions Engineer to lead client AI initiatives from discovery through production deployment. You will partner with clients to identify high-impact Generative AI use cases, evaluate data readiness, and rapidly build proof-of-concept applications that demonstrate tangible business value. You will design and implement production-ready AI solutions leveraging Amazon Bedrock, foundation models, RAG pipelines, and AI agent frameworks such as LangChain and LlamaIndex. As a client-facing technologist, you will translate business requirements into technical architecture recommendations and guide clients on AI/ML best practices.

Location: This can be a remote opportunity, with 2 weeks of travel into Rochester, NY per quarter

What this role is responsible for:

AI Assessment & Discovery

GenAI Solution Development

Client Engagement & Delivery

What makes someone successful in this role:

Requirements:

• Participate in client discovery workshops and technical interviews to identify and prioritize high-impact GenAI use cases
• Analyze client data landscapes, evaluating data readiness, quality, and accessibility for AI solutions
• Rapidly design and build proof-of-concept (POC) applications and live demonstrations that validate AI use cases and illustrate business value to client stakeholders
• Translate discovery findings into technical specifications, architecture recommendations, and implementation plans
• Present POC results and assessment recommendations to client teams, building confidence and momentum for production investments
• Design and implement production-ready Generative AI applications using Amazon Bedrock, Anthropic Claude, and other foundation models
• Build and optimize RAG (Retrieval-Augmented Generation) pipelines with vector databases (Weaviate, OpenSearch, Pinecone)
• Develop AI agents and multi-agent orchestration systems using frameworks like LangChain, LlamaIndex, or custom implementations
• Create conversational AI interfaces with natural language understanding, intent detection, and context management
• Implement prompt engineering strategies, few-shot learning, and fine-tuning approaches for domain-specific applications
• Translate business requirements into technical specifications and suggested implementation plans
• Provide technical guidance and recommendations to clients on AI/ML best practices
• Document architecture decisions, code, and deployment suggestions
• You have a proven track record delivering production AI applications from concept to deployment
• You excel at conducting technical discovery and assessment work, including stakeholder workshops and use-case identification
• You can build proof-of-concept applications and live demonstrations that communicate technical concepts to non-technical audiences
• You have excellent problem-solving skills and the ability to work independently with minimal supervision
• You possess strong written and verbal communication skills for client-facing interactions
• You are passionate about Generative AI and stay current with the latest developments in LLMs, agents, and AI frameworks
• 5+ years of software engineering experience with at least 2+ years focused on AI/ML, data engineering, or cloud-native development
• 2+ years of hands-on AWS experience with production deployments
• 1+ years of direct Generative AI experience (LLMs, embeddings, RAG, agents)
• AWS Certifications: Solutions Architect Associate/Professional, Machine Learning Specialty, or Developer Associate (preferred)
• Background in healthcare, financial services, or regulated industries with understanding of compliance requirements (HIPAA, PCI-DSS, SOC 2) (preferred)
• Contributions to open-source AI/ML projects or published technical content (preferred)
• Experience with multi-tenant SaaS architectures and data isolation patterns (preferred)
• Knowledge of cost optimization strategies for AI workloads (model selection, caching, batching) (preferred)
• Familiarity with frontend frameworks (React, Angular) for building AI-powered UIs (preferred)