Senior AI Software Engineer – AWS & Enterprise Integration

Senior AI Software Engineer – AWS & Enterprise Integration

Location: Remote – United States

Travel: Up to 25%

Employment Type: Direct Hire

Compensation: $150,000–$175,000 Base Salary

Position Overview

Our client, an established and growing IT consulting and managed services organization, is seeking a Senior AI Software Engineer to develop, deploy, and integrate AI-powered solutions across its client environments.

This is a hands-on engineering role for someone who combines strong Python development, applied AI engineering, AWS cloud expertise, and enterprise application integration with the ability to understand complex business challenges and deliver practical solutions.

The organization has an established AWS-based AI architecture supporting multiple clients. Working closely with a Principal Solutions Architect and internal engineering teams, this individual will implement and enhance AI applications, integrate them with existing business systems, and help clients improve efficiency, accuracy, and operational performance.

The ideal candidate enjoys more than writing code. They are comfortable working directly with business leaders, manufacturing teams, finance departments, and IT professionals to understand how organizations operate and identify where AI and automation can deliver measurable value.

This position is remote within the United States, with travel up to 25% for client meetings, discovery sessions, implementations, and other business needs.

Key Responsibilities

AI Application Development & Integration

• Design, develop, test, and deploy production-ready AI applications using Python.
• Build and enhance solutions leveraging large language models (LLMs), retrieval-augmented generation (RAG), and AI agents.
• Develop AI-powered workflows that automate business processes and interact securely with enterprise applications.
• Integrate AI solutions with ERP systems, Microsoft SharePoint, document management platforms, and other business applications.
• Build and maintain REST APIs, asynchronous services, and application integrations using FastAPI or similar frameworks.
• Implement vector search, embeddings, document processing, and retrieval optimization.
• Develop testing and evaluation processes to measure AI accuracy, reliability, performance, and business impact.
• Troubleshoot production issues and continuously improve deployed solutions.

AWS Cloud Engineering & Deployment

• Deploy and maintain AI applications within established AWS cloud environments.
• Utilize AWS services including Lambda, ECS, RDS/Aurora PostgreSQL, SQS, KMS, Secrets Manager, and Amazon Bedrock.
• Develop infrastructure-as-code solutions using Terraform, AWS CDK, or comparable technologies.
• Build and maintain CI/CD pipelines supporting automated testing, deployment, and recovery.
• Ensure applications are scalable, secure, maintainable, and consistently deployed across multiple client environments.
• Collaborate with architecture and engineering teams to follow established technical standards and development practices.
• Maintain technical documentation, deployment procedures, and operational runbooks.

Business Analysis & Client Collaboration

• Partner directly with clients to understand business operations, existing workflows, technical environments, and operational challenges.
• Analyze complex business problems, identify root causes, and determine where AI or automation can provide meaningful improvements.
• Translate business requirements into practical technical solutions with clearly defined outcomes.
• Work with stakeholders across manufacturing, operations, finance, IT, and executive leadership.
• Evaluate existing data sources, application integrations, and business processes to identify opportunities for optimization.
• Communicate technical concepts, recommendations, project risks, and implementation plans to technical and nontechnical audiences.
• Support clients throughout discovery, development, implementation, testing, and adoption.
• Establish measurable success criteria and evaluate whether implemented solutions achieve the intended business results.

Security, Compliance & Reliability

• Build secure AI applications that protect sensitive client and business information.
• Implement appropriate authentication, authorization, access controls, and data isolation.
• Establish safeguards for AI agents and automated workflows, including controlled permissions and human approval processes.
• Apply secure development practices across applications, databases, AI models, APIs, and cloud infrastructure.
• Support regulated client environments with requirements involving CMMC Level 2, NIST 800-171, SOC 2, and applicable export-control regulations.
• Implement appropriate logging, monitoring, auditing, and data protection practices.

Required Qualifications

• 5+ years of professional software engineering experience, with strong hands-on Python development.
• 1+ year of experience developing and deploying LLM-based applications into production environments.
• Practical experience with RAG, vector databases, embeddings, and AI application evaluation.
• Experience developing AI agents, tool-calling workflows, or automated business processes with appropriate security controls.
• Strong experience building REST APIs and integrating enterprise applications.
• Hands-on experience with AWS cloud infrastructure, including deploying and supporting production applications.
• Experience with infrastructure as code, automated deployments, and CI/CD practices.
• Understanding of AI application security, access controls, and sensitive data protection.
• Experience working directly with clients or business stakeholders to gather requirements and deliver technical solutions.
• Strong analytical and critical-thinking abilities with demonstrated success solving complex operational or technical problems.
• Excellent communication and interpersonal skills, with the ability to collaborate across technical teams, business departments, and client organizations.
• Ability to work independently, manage multiple priorities, and contribute within an established technical architecture.
• Must reside in the United States and meet applicable US-person eligibility requirements for access to controlled or export-restricted environments.
• Ability to travel up to 25% as business needs require.

Preferred Qualifications

• Experience with Amazon Bedrock, LiteLLM, Bifrost, or comparable AI model management platforms.
• Knowledge of PostgreSQL, pgvector, Amazon OpenSearch, or other vector search technologies.
• Experience with Model Context Protocol (MCP), multi-agent systems, or complex AI workflow orchestration.
• Experience integrating AI applications with ERP systems, particularly in manufacturing, distribution, or business services.
• Familiarity with Microsoft SharePoint, document management platforms, and IT service management systems.
• Experience with Open WebUI, Langfuse, OpenTelemetry, or similar AI observability and monitoring tools.
• Experience supporting regulated environments involving CMMC, NIST, SOC 2, or export controls.
• Background in IT consulting, managed services, or client-facing technology delivery.
• AWS certifications in Solutions Architecture, Development, Machine Learning, AI, or Security.

What Makes Someone Successful in This Role

The strongest candidates combine technical engineering expertise with a consultative approach to solving business problems.

They can independently build and deploy AI applications while also working effectively with clients to understand the business need behind each solution.

Success requires:

• Analytical Problem-Solving: Ability to break down complex challenges, identify root causes, and develop effective solutions rather than simply implementing requested features.
• Business Understanding: Ability to connect technical decisions to improvements in productivity, operational efficiency, accuracy, and overall business performance.
• Client Communication: Confidence working with business executives, operations leaders, manufacturing personnel, and IT teams.
• Collaboration: Willingness to work within established architectures, accept feedback, and partner closely with other engineers and stakeholders.
• Ownership & Accountability: Ability to take solutions from initial requirements through deployment, troubleshooting, and measurable outcomes.
• Adaptability: Comfortable working across multiple clients, unfamiliar business processes, and evolving technical requirements.

Ideal Candidate Profile

The ideal candidate is a Senior Software Engineer who has transitioned into applied AI engineering and has successfully deployed AI applications in real production environments.

They bring strong Python development skills, hands-on AWS experience, and a solid understanding of LLM applications, RAG architectures, AI agents, and enterprise integrations.

Just as importantly, they enjoy working directly with clients, understanding operational challenges, and translating complex business requirements into technology solutions.

This is an excellent opportunity for an engineer who wants to combine AI development, cloud engineering, enterprise automation, and technical consulting while helping organizations implement practical AI solutions that deliver meaningful business results.

US Work Authorization & Eligibility

Due to the nature of the client's work, including potential access to Controlled Unclassified Information (CUI), export-controlled systems, and regulated environments, candidates must qualify as a US Person under applicable federal regulations.

Eligible individuals include US citizens, US nationals, lawful permanent residents, and qualifying protected individuals, including refugees and asylees.

The client does not provide employment visa sponsorship now or in the future.

Eligibility for access to controlled systems will be determined in accordance with applicable federal regulations and client requirements.