AI Solutions Engineer (Python, DevOps, Cloud, LLM)

Role: AI Solutions Engineer (Python, DevOps, Cloud, LLM)

Location: Dallas, TX (Onsite)

Type: Contract

Description:

POSITION OVERVIEW: AI Solutions Engineer (Python, DevOps, Cloud, LLM)

As an AI Solutions Engineer, you are a hands-on engineer responsible for deploying, configuring, and operating the Client's AMS AI Platform in customer environments. You will work directly with customer engineering teams to install the platform, integrate it into existing DevOps workflows, ingest and analyze codebases, and ensure the solution delivers real, measurable value.

Key Responsibilities

• Platform Installation & Engineering
• Deploy and configure the AMS AI Platform within customer cloud environments.
• Install, validate, and operate platform components using modern DevOps practices (Kubernetes, Helm, Git-based deployments).
• Validate network connectivity, security configurations, logging, and monitoring.
• Manage versioned deployments, upgrades, and rollback procedures using Git-driven release workflows.
• Test end-to-end platform functionality using representative repositories and workloads.
• Operate and maintain monitoring, logging, and alerting to ensure platform health post-deployment.
• Code & Workflow Enablement
• Review customer codebases (language-agnostic) to assess structure, dependencies, and readiness for AI-driven analysis.
• Guide teams on repository hygiene, modularization, access controls, and branching strategies.
• Integrate the platform into customer CI/CD pipelines and release governance models.
• Troubleshoot ingestion, analysis, and runtime issues across code, infrastructure, and integrations.
• Technical Discovery & Solutioning
• Lead technical discovery sessions to understand customer architecture, development workflows, and constraints.
• Translate business and engineering goals into practical platform configurations and deployment patterns.
• Provide architectural guidance and clearly communicate trade-offs when preparing systems for AI-driven workflows.
• Identify risks early (security, scale, complexity) and adjust implementation approach accordingly.
• Customer & Internal Collaboration
• Work directly with customer engineers to drive adoption and unblock progress.
• Partner with Product, Support, and Engineering to surface platform gaps, edge cases, and improvement opportunities.
• Document configurations, patterns, and lessons learned to improve future deployments.
• Serve as the technical point of continuity across installation, enablement, and early production usage.

Basic Qualifications:

• Bachelor's degree in Computer Science, Information Technology, or a related field, or equivalent practical experience.
• Minimum 2+ years of experience in a technical implementation or solutions engineering role, preferably with a SaaS product.
• Minimum 2+ years of experience with Azure, OpenAI, and other cloud and LLM providers
• Minimum 7+ years strong in software development lifecycle (SDLC) practices and common source control workflows (e.g. Git branching strategies, pull requests, release versioning).
• Ability to provide architectural guidance and communicate trade-offs when preparing code for analysis workflows.
• Minimum 5+ years of strong understanding of software integration principles and APIs.
• Comfortable reading code at a structural level, with proficiency in at least one code language.
• Excellent problem-solving and analytical skills.
• Exceptional communication and interpersonal skills, with the ability to explain complex technical concepts to non-technical audiences.
• Proven ability to manage multiple projects simultaneously and prioritize effectively.
• Ability to travel to client sites as needed

Key Skills

• Programming proficiency in Python, R, and Java
• Relevant AI libraries (TensorFlow, PyTorch).
• Machine Learning and Deep Learning Expertise
• Data Management and Processing.
• Software engineering fundamentals including architecture, systems thinking, API integration, and CI/CD practices. Knowledge of cloud computing platforms (AWS, Azure, Google Cloud) for scaling and deployment. Understanding machine learning workflows and algorithms.

Degree: Bachelor's or Master's in Computer Science or related field.

Nice to Have; (But not a must)

• SaaS products