Business Intelligence & Reporting Data Engineer Lead (Agentic AI/AI)

Cynet Systems•Reston, VA

About The Position

The Business Intelligence & Reporting Data Engineer Lead will play a crucial role in building and deploying AI agents to automate and optimize labor-intensive workflows. This position will enable data-driven decision-making through business intelligence and reporting solutions. Responsibilities include writing software code for AI agent communication, connecting models and agents to internal and external services via APIs, and developing data integration pipelines and reporting solutions. The role also involves supporting testing, debugging, deployment, monitoring, and ensuring the reliable execution of agentic AI systems. The lead will utilize a combination of open-source models, agentic frameworks, machine learning technologies, business intelligence platforms, and proprietary commercial AI models. Key aspects include securing agentic workflows, evaluating results for accuracy, performance, and business impact, developing dashboards and reports to measure solution effectiveness and operational outcomes, and ensuring AI systems adhere to ethical AI principles. The role also involves research, prototype development, outcome evaluation, documentation, and presenting insights to various audiences.

Requirements

3+ years of experience building production-level AI or ML systems, including LLMs, AI agents, or complex automation frameworks.
3+ years of experience with Python and Python libraries such as Pandas, NumPy, and related data processing tools.
Experience with Large Language Models (LLMs), Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL).
Strong understanding and hands-on experience with Natural Language Processing (NLP).
Experience with AI agent frameworks and tools such as LangGraph, LangChain, TensorFlow, or PyTorch.
Experience optimizing workflows through intelligent automation and AI-driven solutions.
Experience integrating AI agents with APIs, cloud platforms, enterprise applications, and databases.
Experience building and deploying AI pipelines on AWS SageMaker with Aurora PostgreSQL for data ingestion, analytics, and operational reporting.
Experience performing automated testing, troubleshooting, and application maintenance.
Experience with Software Development Lifecycle (SDLC) methodologies, including DevSecOps practices.
Experience with cloud platform - Amazon Web Services (AWS).
Experience developing RESTful services using Java and Spring Boot.
Experience managing CI/CD pipelines and containerized deployments using Kubernetes, Docker, Jenkins, or similar technologies.
3+ years of experience designing and developing business intelligence solutions and enterprise reporting platforms.
Experience creating interactive dashboards and reports using tools such as Power BI, Tableau, or similar BI platforms.
Hands-on expertise with SQL, data querying, and relational database concepts.
Experience developing data models, semantic layers, KPIs, scorecards, and performance metrics for business stakeholders.
Experience integrating and transforming data from multiple enterprise systems and APIs for reporting and analytics purposes.
Knowledge of data warehousing, ETL/ELT processes, data governance, and data quality management.
Ability to analyze business requirements and translate them into meaningful visualizations and actionable insights.
Experience building operational, executive, and analytical reports that support strategic decision-making.
Understanding of data visualization best practices, reporting standards, and storytelling with data.
Experience measuring and reporting AI solution performance, adoption metrics, operational efficiencies, and business outcomes.
Strong verbal and written communication skills with the ability to present technical and analytical findings to both business and executive audiences.

Nice To Haves

Preferred experience using GitHub and collaborative development platforms.
Experience combining AI/ML capabilities with BI platforms to deliver intelligent reporting and decision-support solutions.
Experience with Azure Data Factory, Databricks, Snowflake, Redshift, Synapse, or similar modern data platforms.
Familiarity with MLOps, DataOps, and analytics engineering practices.
Experience implementing AI-powered insights, predictive analytics, recommendation systems, or conversational analytics solutions.

Responsibilities

Collaborate with Data Scientists, Product Managers, Business Analysts, and stakeholders to build and deploy AI agents that automate and optimize labor-intensive workflows.
Enable data-driven decision making through business intelligence and reporting solutions.
Write software code to support AI agent communication.
Connect models and agents to internal and external services via APIs.
Develop data integration pipelines and design reporting solutions that provide actionable business insights.
Support testing, debugging, deployment into target environments, monitoring, and ensuring reliable execution of agentic AI systems.
Utilize a combination of open-source models, agentic frameworks, machine learning technologies, business intelligence platforms, and proprietary commercial AI models.
Secure agentic workflows and evaluate results for accuracy, performance, and business impact.
Develop dashboards and reports that measure solution effectiveness and operational outcomes.
Ensure AI systems adhere to ethical AI principles, including transparency, fairness, security, and responsible AI practices.
Conduct research, develop prototypes, evaluate outcomes, document findings, and present insights to business and technical audiences.