"Alerted.org

Job Description

We are seeking a motivated, career- and customer-oriented SME AI Ops Engineer to join our AI innovation team in Ashburn, VA. This is currently a hybrid position with 2-3 days onsite/week. While some formal training is available for mission knowledge and data-driven solutions, much of this learning occurs through on-the-job training and individual initiative. Therefore, we are looking for self-starters who can independently explore and acquire knowledge, build informal support networks, and appreciate the significant challenge of securing the Nation's borders. Responsibilities include but are not limited to:

•- Lead the design and operational deployment of Large Language Model (LLM) based applications and AI agents across AWS or Google cloud environments.

•- Implement and optimize Model Context Protocol (MCP) for context-aware orchestration and efficient scaling of AI services.

•- Design and manage embeddings generation, storage, and retrieval, enabling semantic search and recommendation and develop monitoring, logging, and observability solutions leveraging the Elastic Stack (Elasticsearch, Logstash, Kibana).

•- Engineer graph-oriented deployment architectures for dependency mapping, relationship-aware data flow advanced troubleshooting and automate deployment, scaling, and monitoring using modern MLOps and AIOps practices (CI/CD, containerization, orchestration).

•- Ensure data quality, lineage, and governance across multiple systems and pipelines

•- Collaborate with data scientists, ML engineers, and DevOps teams to deliver high-availability AI-powered applications.

•- Build monitoring and alerting solutions to detect anomalies, performance bottlenecks, and failures in AI/ML services and research and implement emerging AI infrastructure and tools and best practices to continuously improve system reliability and scalability.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to [email protected] learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

Skills and Requirements

-Active Top Secret Clearance and ability to obtain public trust

- BS/BA and 12+ years or MS/MA/MBA and 9+ years or PhD/Doctorate and 7+ years.

- Experience in Data Engineering, DevOps, or AI/ML Ops roles.

- Strong coding skills in Python or Java.

- Hands-on experience with vector search and retrieval-augmented generation (RAG).

- Knowledge of security, compliance, and access controls for data and AI systems.

- Strong foundation in data engineering and distributed systems.

- Deep understanding of search, indexing, and graph databases (Elasticsearch, Neo4j, Janusgraph or equivalent).

- Experience with LLM deployment frameworks (LangChain, RAG pipelines, vector databases like Pinecone, Weaviate, or FAISS).

- Proficiency with cloud platforms and infrastructure as code.

- Hands-on with containers and orchestration (Docker, Kubernetes).

- Familiarity with monitoring and observability tools (Grafana, ELK).

- Knowledge of CI/CD pipelines (Harness, Jenkins) and best practices for AI/ML deployment. - Strong foundation in data engineering and distributed systems.

- Deep understanding of search, indexing, and graph databases (Elasticsearch, Neo4j, Janusgraph or equivalent).

- Experience with LLM deployment frameworks (LangChain, RAG pipelines, vector databases like Pinecone, Weaviate, or FAISS).

- Proficiency with cloud platforms and infrastructure as code.

- Hands-on with containers and orchestration (Docker, Kubernetes).

- Familiarity with monitoring and observability tools (Grafana, ELK).

- Knowledge of CI/CD pipelines (Harness, Jenkins) and best practices for AI/ML deployment.