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

Job Overview:

Pay Range: $88.18hr - $93.18hr

Requirement/Must Have:

• 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.

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.

Nice to Have:

• 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.

Skills:

• Python.
• Pandas.
• NumPy.
• LLM.
• Machine Learning.
• Deep Learning.
• Reinforcement Learning.
• NLP.
• LangGraph.
• LangChain.
• TensorFlow.
• PyTorch.
• AWS SageMaker.
• Aurora PostgreSQL.
• SDLC.
• DevSecOps.
• AWS.
• Java.
• Spring Boot.
• Kubernetes.
• Docker.
• Jenkins.
• Power BI.
• Tableau.
• SQL.
• ETL/ELT.