AI Solutions Engineer

Our client a growing IT consulting and managed services organization is seeking a highly analytical collaborative and technically strong Senior AI Software Engineer to help design deploy and integrate AI-driven solutions across its customer base.

This is not a traditional software development position. The ideal candidate combines strong Python development AI engineering AWS cloud experience and business systems integration with exceptional analytical thinking problem-solving and interpersonal skills.

The ability to analyze complex business problems work effectively with others and apply technical solutions to real-world operational challenges is critical to success in this role.

The selected candidate will work directly with client leadership operations teams manufacturing personnel finance departments and IT professionals to understand business processes identify inefficiencies and implement AI solutions that improve productivity accuracy and operational performance.

The organization has an established AWS-based AI architecture that is deployed across multiple client environments. This individual will be responsible for implementing configuring integrating and improving solutions within that established framework while collaborating with the Principal Solutions Architect.

This is a hands-on engineering and consulting role requiring technical depth independent problem-solving and the ability to build strong relationships with both technical and nontechnical stakeholders.

• Work directly with clients to understand their business operations processes challenges and technology environments.
• Apply strong analytical and critical-thinking skills to evaluate complex problems identify root causes and develop practical solutions.
• Collaborate with business leaders plant managers finance teams IT personnel and end users to identify opportunities for AI and automation.
• Translate business requirements into technical solutions that deliver measurable improvements.
• Evaluate existing workflows data sources and system integrations to determine how AI can improve efficiency and decision-making.
• Ask the right questions challenge assumptions and use data to validate proposed solutions.
• Communicate technical concepts project risks and recommendations clearly to both technical and nontechnical audiences.
• Work collaboratively with internal engineering teams and client stakeholders throughout discovery development implementation and adoption.
• Develop acceptance criteria document business requirements and measure solution effectiveness.

• Design develop test and deploy production-ready AI applications using Python.
• Build and maintain AI applications utilizing large language models (LLMs) retrieval-augmented generation (RAG) and agent-based automation.
• Develop AI agents capable of interacting with business applications and executing controlled workflows.
• Build secure integrations between AI applications and ERP systems document management platforms Microsoft SharePoint and other enterprise applications.
• Develop and maintain REST APIs and asynchronous services using FastAPI or equivalent frameworks.
• Implement vector search embeddings document processing hybrid search and retrieval optimization.
• Create measurable testing and evaluation processes to validate AI-generated responses retrieval accuracy and solution reliability.
• Implement appropriate permissions security controls and human approval processes for AI-driven actions.
• Troubleshoot technical issues evaluate performance and continuously improve deployed solutions.

• Deploy AI solutions within AWS cloud environments using Terraform AWS CDK or equivalent infrastructure-as-code tools.
• Work with AWS services such as Lambda ECS RDS/Aurora PostgreSQL SQS KMS and Secrets Manager.
• Support deployments utilizing Amazon Bedrock or comparable managed AI platforms.
• Develop and maintain CI/CD pipelines for automated testing deployment and rollback.
• Maintain consistent repeatable deployments across multiple client environments.
• Collaborate with architects and engineering teams to maintain technical standards security scalability and reliability.
• Document technical configurations architecture decisions deployment procedures and operational runbooks.

• Develop secure AI applications that protect sensitive client and business information.
• Understand and implement data isolation access controls authentication and authorization.
• Ensure appropriate handling of sensitive data across AI models databases application logs and integrations.
• Support compliance requirements including CMMC Level 2 NIST 800-171 SOC 2 and applicable export-control regulations.
• Implement logging monitoring auditing and data protection practices.

Candidates must meet the following core requirements:

• Minimum 5 years of professional software engineering experience.
• At least 1 year of hands-on experience delivering LLM-based applications or features into a production environment.
• Strong Python development experience including production applications API development testing and maintainable code.
• Hands-on experience with retrieval-augmented generation (RAG) vector databases embeddings and AI application evaluation.
• Experience developing AI agents or tool-calling workflows with programmatically enforced permissions and controls.
• Strong experience integrating business applications using REST APIs authentication and data synchronization.
• AWS cloud engineering experience including infrastructure as code and repeatable deployments.
• Understanding of AI data security sensitive information handling and access control.
• Demonstrated experience working directly with clients or business stakeholders to gather requirements and implement technical solutions.
• Excellent analytical critical-thinking and problem-solving skills with the ability to apply those skills to real business challenges.
• Strong interpersonal collaboration and communication skills with the ability to work effectively across departments and technical skill levels.
• Ability to work independently while following established architecture engineering standards and team processes.
• Must be based in the United States and meet US-person eligibility requirements for access to controlled or export-restricted systems.

• Experience with Amazon Bedrock LiteLLM Bifrost or comparable AI model gateways.
• Experience with PostgreSQL pgvector Amazon OpenSearch or comparable vector search platforms.
• Experience with Model Context Protocol (MCP) AI agents or multi-step automated workflows.
• Experience integrating AI solutions with ERP systems particularly within manufacturing distribution or business-services environments.
• Experience with Microsoft SharePoint document management systems or ITSM platforms.
• Experience with Open WebUI Langfuse OpenTelemetry or similar tools.
• Understanding of CMMC Level 2 NIST 800-171 SOC 2 or regulated IT environments.
• Experience delivering technical solutions for multiple clients within a consulting or managed services organization.
• AWS certifications in Solutions Architecture Development Machine Learning AI or Security.

These are major differentiators in the selection process and should be evaluated as carefully as technical skills.

Analytical Thinking: Ability to break down complicated business problems evaluate information identify root causes and recommend logical actionable solutions.

Applied Problem-Solving: Must demonstrate examples of using analytical skills to solve operational or technical problems not simply identifying issues or recommending theoretical solutions.

Collaboration & Teamwork: Ability to work effectively with engineers architects management and client personnel. Must be comfortable receiving feedback working within established standards and contributing to shared goals.

Business Acumen: Ability to understand how technology impacts manufacturing finance operations and business performance.

Communication: Comfortable explaining technical solutions to business executives operational managers and end users.

Initiative & Accountability: Takes ownership of assignments follows through on commitments and measures success based on business outcomes.

Adaptability: Comfortable working across multiple client environments learning unfamiliar business processes and adjusting to changing project requirements.

Consultative Mindset: Interested in understanding the clients challenges before recommending or developing solutions.

The ideal candidate is a senior software engineer who has evolved into AI engineering and enjoys solving complex business problems.

This person has successfully built and deployed AI applications understands AWS cloud architecture and has experience integrating technology with enterprise business systems.

Equally important this person enjoys working with people.

They are comfortable sitting down with a manufacturing manager finance director or operations leader to understand a business problem analyzing the available data and determining how AI or automation can improve the process.

They are not simply interested in writing code. They want to understand why a solution is needed how it will be used and whether it delivers the intended business result.

The strongest candidates will demonstrate a combination of technical expertise analytical ability business understanding communication skills and a collaborative approach to problem-solving.

US Work Authorization & Sponsorship Requirements

Due to the nature of our clients work including access to Controlled Unclassified Information (CUI) export-controlled systems and compliance requirements under CMMC Level 2 ITAR and EAR regulations candidates must qualify as a US Person under applicable federal regulations.

Eligible candidates include US citizens US nationals lawful permanent residents (Green Card holders) and qualifying protected individuals including refugees and asylees.

Our client does not provide employment visa sponsorship and is unable to sponsor candidates now or at any time in the future. Candidates requiring H-1B L-1 TN F-1 OPT/CPT or other employment-based visa sponsorship are not eligible for consideration.

General authorization to work in the United States does not independently satisfy the US-person requirement. Eligibility will be determined in accordance with applicable federal export-control regulations.