AI Engineer

Position Overview

AKIVA is seeking AI Engineers across multiple experience levels (Junior, Mid-Level, Senior, and Subject Matter Expert) to design, build, integrate, and operate production-grade AI and agentic systems supporting enterprise and government programs. This role supports regulated, compliance-driven environments with data-intensive platforms, dashboards, automation workflows, and multi-system integrations critical to program oversight, reporting, and operational efficiency.

Key Responsibilities

• Design, develop, and maintain AI-enabled and agentic systems for enterprise and government use cases
• Build and operate multi-agent workflows, including agent coordination, orchestration, and tool invocation
• Develop secure data pipelines for ingestion, transformation, and validation
• Integrate AI solutions with enterprise platforms using REST APIs, SQL Server, and secure file transfer
• Support production dashboards, automation scripts, and scheduled/overnight processes
• Monitor, troubleshoot, and optimize AI systems, data pipelines, and integrations
• Contribute to or lead DevOps, MLOps, and AgentOps practices (level-dependent)
• Ensure solutions comply with data security, governance, and audit requirements
• Document systems, workflows, and AI behavior for compliance and operational continuity

Required Qualifications

• Experience in software engineering, AI/ML, data engineering, or enterprise systems (commensurate with level)
• Proficiency in Python and SQL (SQL Server preferred)
• Experience working with REST APIs and integrated enterprise systems
• Familiarity with cloud-based environments (Azure, AWS, or equivalent)
• Understanding of AI/ML concepts, including model deployment and lifecycle management
• Exposure to or experience with agent-based systems, orchestration, or workflow automation
• Experience supporting or contributing to production systems

Preferred Qualifications

• Experience with multi-agent systems and agent orchestration frameworks
• Familiarity with agent frameworks (e.g., LangChain, AutoGen, CrewAI, or custom frameworks)
• Experience with monitoring and observability tools
• Familiarity with vector databases, semantic retrieval, and AI-enabled dashboards
• Experience with cognitive architectures, neural network-based models, or hybrid symbolic/LLM systems in production

Technologies & Tools

Languages: Python, SQL, PowerShell/Bash Databases: SQL Server AI & Agentic Systems: LLM-based agents, multi-agent orchestration frameworks, vector databases Integration: REST APIs, FTP/SFTP Cloud: Azure, AWS, GCP DevOps & Ops: CI/CD pipelines, infrastructure as code, monitoring and logging tools Visualization: Power BI, Tableau, or custom dashboards

Methodologies

• Agile / Scrum
• Secure Software Development Lifecycle (SDLC)
• MLOps and AgentOps best practices
• Data governance, auditability, and compliance controls
• Responsible and explainable AI practices