AI Engineer

• Design, develop, and deploy machine learning models and AI-powered applications
• Collaborate with cross-functional teams to identify and implement AI/ML solutions for client needs
• Build and maintain data pipelines, model training workflows, and inference systems
• Evaluate and integrate large language models (LLMs), generative AI tools, and emerging AI frameworks
• Monitor model performance, conduct testing, and iterate on improvements
• Document technical architectures, methodologies, and best practices
• Stay current with AI/ML research and translate advancements into practical applications

• Define and evolve enterprise AI architecture, standards, reference models and implementation guidelines to support consistent solution delivery across business functions
• Align AI initiatives to enterprise strategies, roadmaps, and business priorities
• Identify and prioritize AI opportunities that drive student success and operational efficiency
• Design scalable AI platforms and solutions across data, application, and technology domains
• Provide guidance on how AI, ML, and automation will improve current and future-state architectures
• Lead design and implementation of AI-powered solutions (e.g., LLMs, agents, automation, and analytics)
• Integrate AI capabilities with enterprise applications and data platforms
• Guide evaluation and selection of AI/ML models, frameworks, and vendors
• Establish reusable patterns, APIs, and integration approaches
• Establish governance practices covering model oversight, responsible AI usage, security controls, compliance expectations, and lifecycle management
• Establish AI governance, including responsible AI, security, privacy, and compliance (FERPA, HIPAA, GDPR)
• Establish lifecycle management practices (MLOps/LLMOps, model monitoring, drift detection, retraining)
• Implement performance, bias, and risk monitoring frameworks
• Ensure secure AI architecture (e.g., data protection, isolation, adversarial defense)
• Partner with business, product, data, and engineering teams to provide AI solutions
• Provide architectural guidance to architects and delivery teams
• Facilitate workshops, POCs, demos and AI adoption projects identifying viable opportunities where AI can be a positive differentiator
• Mentor teams on AI best practices, patterns, and frameworks

Enterprise AI Architect

• Architect and deploy secure, distributed platforms supporting modular, multi-service software environments.
• Design and integrate APIs, microservices, and data pipelines that enable interoperability across mission, simulation, and analytical systems.
• Define and enforce architectural principles, modularity standards, and software best practices across development teams.
• Engineer solutions that bridge cloud, on-prem, and classified enclaves, ensuring interoperability and compliance.
• Engineer cloud-native solutions in AWS GovCloud or hybrid IL4–IL6 environments with strict security and accreditation requirements.
• Implement Infrastructure-as-Code (Terraform, CloudFormation, or CDK) to automate provisioning, configuration, and accreditation workflows.
• Build and maintain CI/CD pipelines that embed security, compliance, and automated testing from development through deployment.
• Ensure all platform designs support zero-trust principles, multi-enclave deployment, and repeatable accreditation (ATO) processes.
• Design or oversee large-scale data ingestion, transformation, and orchestration environments (Kafka, Airflow, Spark, or Databricks).
• Shape data architectures that balance throughput, latency, and cost across distributed mission environments.
• Enable secure and efficient data access and synchronization across analytical, AI-enabled, and simulation workloads.
• Transition software and platforms from prototype to production, ensuring maintainability, observability, and configuration consistency.
• Establish automated testing, release governance, and performance monitoring standards for ongoing sustainment.
• Engineer architectures for redundancy and resilience under degraded or disconnected operational conditions.
• Design and maintain secure enclave architectures, boundary controls, and cross-domain integration solutions.
• Collaborate with cybersecurity and ISSM teams to support ATO and ongoing accreditation automation.
• Integrate emerging technologies, open standards, and modern frameworks into operational systems.

Distinguished AI Software Engineer

• Architect and integrate hybrid AI systems that combine traditional machine learning, deep learning, large language models (LLMs), and retrieval-augmented generation (RAG) pipelines.
• Design and deploy scalable AI architectures including APIs, microservices, and model-serving frameworks that integrate seamlessly with analytic, simulation, or operational systems.
• Lead the full AI/ML lifecycle — from data ingestion and feature engineering through training, deployment, and sustainment within secure DoD environments (IL5/IL6, ATO, GovCloud).
• Engineer event-driven data pipelines and feature stores for both structured and unstructured data, including text, imagery, and simulation outputs.
• Ensure Responsible AI practices by embedding traceability, explainability, and confidence scoring into deployed systems.
• Implement and maintain MLOps pipelines (MLflow, Kubeflow, Airflow, Docker/Kubernetes) to support continuous integration, retraining, and drift detection.
• Transition R&D prototypes into production, optimizing for mission constraints such as limited compute, edge environments, or disconnected operations.
• Provide technical leadership and mentorship, setting standards for model quality, architectural design, and ethical AI deployment across programs.
• Collaborate across engineering, data, and modeling teams to unify FTI’s AI portfolio, ensuring interoperability and reuse across mission systems.
• Support proposal and solution development, providing technical inputs for AI/ML architectures, data strategies, and Responsible AI assurance frameworks.

Distinguished AI/ML Engineer

• Own and develop enterprise accounts across the Southeast (GA, FL, TN, NC, SC, AL)
• Build 50%+ of pipeline through cold outreach, events, networking, and partner collaboration
• Manage complex enterprise sales cycles ($75K-$200K ACV, 3-9 months) with multiple stakeholders
• Sell to CIOs, COOs, CFOs, and operations leaders at manufacturers, financial institutions, and enterprise companies
• Run discovery, demos, POCs, business case development, and contract negotiation
• Partner with channel ecosystem to co-develop and accelerate opportunities
• Maintain Salesforce CRM hygiene, pipeline accuracy, and quarterly forecasting
• Travel across Southeast territory 20-40%

Enterprise Sales Executive – AI Platform

• Build & Integrate: Design and implement full-stack features that utilize LLMs to solve user problems.
• Streaming Interfaces: Build low-latency interfaces that stream text to the client.
• Roadmap Ownership: Leading a team of developers to build new features.
• Write robust, high-performance Go code to orchestrate AI services.
• Design and implement RESTful and gRPC APIs.
• Collaborate on integrating Kafka and Redis to build asynchronous message processing pipelines.
• Use AI coding assistants to accelerate code generation, testing, and documentation.
• Critically evaluate and debug AI-generated code.
• Refine system prompts and write automated evaluations for AI performance.

AI Engineer jobs in Alabama

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AI Engineer

Enterprise AI Architect

Distinguished AI Software Engineer

Distinguished AI/ML Engineer

Enterprise Sales Executive – AI Platform

Staff AI Engineer