UPDATED JD:: Agentic AI Engineer / AI Developer (with Business Intelligence & Reporting)
The Agentic AI Engineer will collaborate with Data Scientists, Product Managers, Business Analysts, and stakeholders to build and deploy AI agents that automate and optimize labor-intensive workflows while enabling data-driven decision making through business intelligence and reporting solutions.
Responsibilities include writing software code to support AI agent communication, connecting models and agents to internal and external services via APIs, developing data integration pipelines, and designing reporting solutions that provide actionable business insights. The engineer will support testing, debugging, deployment into target environments, monitoring, and ensuring reliable execution of agentic AI systems.
The role requires utilizing a combination of open-source models, agentic frameworks, machine learning technologies, business intelligence platforms, and proprietary commercial AI models. The engineer will also secure agentic workflows, evaluate results for accuracy, performance, and business impact, and develop dashboards and reports that measure solution effectiveness and operational outcomes.
The individual will ensure AI systems adhere to ethical AI principles, including transparency, fairness, security, and responsible AI practices. They should be a self-starter who works well within a collaborative team environment, actively sharing discoveries, seeking feedback, and supporting organizational learning. The role may also involve conducting research, developing prototypes, evaluating outcomes, documenting findings, and presenting insights to business and technical audiences.
Required Skills
I & Software Engineering
3+ years of experience building production-level AI or ML systems, including LLMs, AI agents, or complex automation frameworks.[KN1.1]
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).[KN2.1]
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.[KN3.1]
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).[KN4.1]
Experience developing RESTful services using Java and Spring Boot.
Experience managing CI/CD pipelines and containerized deployments using Kubernetes, Docker, Jenkins, or similar technologies.
Preferred experience using GitHub and collaborative development platforms.
Business Intelligence & Analytics
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.[KN5.1]
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 communication skills with the ability to present technical and analytical findings to both business and executive audiences.
Preferred Qualifications
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.
This version positions the role as a hybrid AI Engineer + Business Intelligence Developer, to build AI solutions while also delivering executive reporting, KPI dashboards, and measurable business outcomes.
