Integration Engineer 2 develops and implements interfaces between BMC applications, utilizing various tools and standards. Works closely with vendors and application analysts to ensure data flow consistency.
About the role
- Senior AI Solutions Architect Lead designing and implementing enterprise AI architectures. Collaborating with engineers and government partners for national security outcomes.
Responsibilities
- Design and implement end-to-end AI architectures that meet mission, performance, and security requirements
- Define system topology, data flows, model serving patterns, and integration points using scalable frameworks
- Select and configure AI platforms and model orchestration tools to ensure high availability and low latency
- Collaborate with software, data, and platform engineers to validate architecture decisions and optimize deployment patterns
- Ensure compliant, production-grade implementation of AI capabilities across classified and unclassified environments
- Lead a team of 8-15 direct reports, providing mentorship and guidance to enhance team performance
- Develop and maintain a System Engineering Plan (SEP) to manage all systems architecture aspects
- Conduct systems engineering activities to specify, build, and maintain system engineering designs
- Manage requirements and maintain a system requirements management environment
- Support enterprise system architecture activities to define and scope the AI solutions
- Define, document, and maintain APIs and technical standards for interoperability
- Engineer and continuously improve the underlying infrastructure of the AI platform
- Identify and integrate government, commercial, and open-source tools into the AI environment
- Design and enhance user interface (UI) and user experience (UX) components of the platform
- Implement and maintain services for production-ready AI/ML models
- Ensure cybersecurity compliance and maintain cybersecurity architecture for the AI system
- Perform site reliability engineering to maintain a reliable and efficient AI platform
Requirements
- Active Top Secret (TS) clearance with SCI eligibility
- Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related technical discipline and 8–12 years of relevant experience OR Master’s degree in a related field and 6–10 years of relevant experience
- Minimum of 10 years of experience in systems engineering and AI and/or data intelligence architectures
- Experience architecting and deploying enterprise AI/ML solutions in cloud environments (AWS, Azure, or GCP)
- Experience designing and delivering AI/ML solutions in enterprise cloud environments (AWS, Azure, or GCP)
- Experience integrating AI/ML capabilities into production systems using APIs and microservices architectures
- Experience developing AI/ML pipelines including data preparation, model training, validation, and deployment
- Experience working across cross-functional teams to deliver integrated technical solutions
- Experience operating within SAFe or large-scale Agile frameworks supporting enterprise systems
- Experience with system architecture design and implementation in classified environments
- Experience developing Agentic AI solutions such as autonomous planning–execution–reflection loops, multi-agent collaboration and coordination, and tool usage patterns including API integration, retrieval-augmented generation (RAG), and memory/context management)
- Experience using vector databases (e.g., Pinecone, Weaviate, FAISS)
- Demonstrated experience leading and mentoring technical engineering teams
- Strong understanding of AI/ML technologies and their application in enterprise environments
- Strong understanding of AI/ML frameworks (e.g., PyTorch, TensorFlow) and data engineering concepts
- Solid understanding and hands-on experience with generative AI models such as prompt engineering, chain-of-thought reasoning, and Natural Language Processing (NLP) tasks such as entity extraction, summarization, and semantic search
- Working knowledge of Large Language Models (LLMs) and agent frameworks such as LangChain, LangGraph, CrewAI, A2A, MCP, or AutoGen
- Familiarity with deployment into virtualized and containerized environments (e.g., VMware, Docker, Kubernetes)
Benefits
- Health and Wellness programs
- Income Protection
- Paid Leave
- Retirement
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