Agentic AI Solutions

SAIC • Washington, DC

14h

About The Position

SAIC is seeking a high performing group of individuals for AI genetic solution to work closely with our government customer. We’re building a powerful team of forward-thinking professionals skilled in Agentic AI solutions, cutting-edge engineering, and scalable cloud architectures. These roles may not be immediately available. We’re seeking motivated, multidisciplinary individuals to join one of the following key roles within our team: Agentic AI Solutions Product Manager, Agentic AI Solutions Orchestrator, Agentic AI-Native Engineer (Mid to Senior), Agentic AI Quality & Spec Engineer (Mid to Senior), Agentic AI Platform Engineer (Mid to Senior). For these roles we’re looking for individuals with: A deep passion for applying AI, machine learning, and advanced engineering methodologies to practical, real-world problems. Collaborative mindsets with the ability to work across cross-disciplinary teams, including data science, product management, engineering, and client stakeholders. A strong grasp of large language models (LLMs), AI governance, and advanced frameworks like LangGraph, CrewAI, Multi-Agent orchestration, MCP, and code generation tools. Experience navigating cloud-native environments like AWS, Azure, and Google Cloud, and an eagerness to stay ahead of emerging technologies.

Requirements

• Agile product management, prompt design, business process transformation, experimentation (e.g., A/B testing), and usage of platforms such as LLMs, RAG, MCP, and other multi-agent systems.
• 8+ years in product/program management, digital/cloud transformation, including 3+ years in AI solutions and adoption.
• Strong systems thinking, prompt design, multi-agent framework architecture, and experience with cloud-native platforms.
• 8+ years in IT solutions architecture, with 3–5+ years focused on AI, cloud, and automation systems.
• Spec coding, autonomous agents, cloud deployment systems, automated workflows, and LLM integration.
• 5–8+ years in software engineering, cloud-native development, APIs, and AI/automation systems.
• QA automation, testing frameworks, prompt engineering, agentic workflow design, and AI-driven quality engineering.
• 5–8+ years in quality assurance, SDLC, and automation.
• Kubernetes/container orchestration, CI/CD automation, FinOps, data pipelines, and DevOps methodologies.
• 5–8+ years in DevOps, cloud/platform engineering, Kubernetes, and AI/LLMOps platforms.

Nice To Haves

• A deep passion for applying AI, machine learning, and advanced engineering methodologies to practical, real-world problems.
• Collaborative mindsets with the ability to work across cross-disciplinary teams, including data science, product management, engineering, and client stakeholders.
• A strong grasp of large language models (LLMs), AI governance, and advanced frameworks like LangGraph, CrewAI, Multi-Agent orchestration, MCP, and code generation tools.
• Experience navigating cloud-native environments like AWS, Azure, and Google Cloud, and an eagerness to stay ahead of emerging technologies.

Responsibilities

• Crafting agent behaviors, workflow orchestration frameworks, and AI governance guardrails.
• Leading the creation of implementation plans, user experiences, and operational workflows for AI solutions.
• Facilitating stakeholder engagement and aligning solution strategies with organizational goals.
• Designing, implementing, and governing multi-agent workflows and architecture(e.g., LangGraph, AutoGen, CrewAI).
• Orchestrating a dynamic portfolio of AI agents, fostering collaboration between multiple agent systems and AIs.
• Building reusable components for rapid deployment and integration with cloud platforms.
• Architecting and deploying agent-based systems with a strong focus on production-grade engineering and continuous improvement.
• Leveraging Model Context Protocol (MCP), CI/CD pipelines, API design, and cloud-native tools for agile, iterative development cycles.
• Critically reviewing and optimizing AI-generated outputs for maximum efficiency, quality, and usability.
• Translating intent into actionable AI-consumable workflows, specifications, blueprints, and test cases.
• Designing intelligent test generation systems to ensure automated decision processes align with business goals.
• Ensuring test cases, validations, and regulations are built into the core of automated AI systems.
• Designing and managing stable, secure agent execution environments, deployment pipelines, and cloud-native infrastructures.
• Implementing robust FinOps, IAM and security protocols, agent governance guardrails, and observability for Agentic AI platforms.
• Leveraging LLMOps platforms for managing complex multi-agent systems and workloads across cloud environments (AWS, Azure, GCP).

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed