Software Engineer I

The Core Infrastructure Compute team is responsible for managing and maintaining enterprise compute inventory, integrating and consolidating data from multiple sources, and ensuring data accuracy, consistency, and visibility across infrastructure platforms. The team focuses on developing modern web applications, building scalable data integration and reporting solutions, leveraging Generative AI capabilities, and delivering actionable insights through advanced analytics and intelligent automation to drive operational efficiency, informed decision-making, and continuous infrastructure optimization.

We are seeking an experienced Senior Software Engineer with strong hands-on expertise in Microsoft Power BI, MS SQL Server, React.js, Python, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and data analytics.

The ideal candidate will possess strong software engineering fundamentals, programming expertise, analytical problem-solving capabilities, and practical experience designing and developing AI-enabled enterprise applications. The role requires the ability to integrate structured and unstructured enterprise data with modern AI technologies while ensuring application scalability, security, reliability, and performance.

Software Engineering & Application Development

• Design, develop, enhance, and maintain scalable enterprise applications, analytical platforms, and AI-enabled solutions.
• Develop modern, responsive, and interactive web applications using React.js, JavaScript, TypeScript, HTML, and CSS.
• Build reusable frontend components, dashboards, and application interfaces.
• Develop and integrate RESTful APIs, backend services, and enterprise data integrations.
• Implement application functionality using Python, JavaScript, TypeScript, C#, or equivalent programming languages.
• Apply software engineering best practices, including modular architecture, clean coding, unit testing, code reviews, and performance optimization.
• Troubleshoot complex application, integration, performance, and production issues.
• Collaborate with product owners, data engineers, AI engineers, and business stakeholders to deliver scalable technical solutions.

Power BI & Data Analytics

• Design, develop, deploy, and maintain enterprise Power BI dashboards, reports, and analytical solutions.
• Translate complex business requirements into interactive dashboards, KPIs, scorecards, and actionable insights.
• Develop Power BI semantic models, advanced DAX measures, calculated columns, and Power Query transformations.
• Integrate Power BI with MS SQL Server, APIs, enterprise applications, and other structured data sources.
• Optimize dashboard performance, data refresh processes, query execution, and reporting efficiency.
• Implement row-level security, workspace access controls, and data governance best practices.
• Analyze large datasets to identify trends, anomalies, operational risks, and opportunities for improvement.
• Develop automated reporting and analytical solutions to improve business decision-making.
• Explore AI-assisted analytics, natural-language querying, and intelligent insights across enterprise data.

MS SQL Server & Database Engineering

• Design, develop, maintain, and optimize SQL Server databases and enterprise data solutions.
• Develop complex T-SQL queries, stored procedures, functions, views, and database scripts.
• Perform query optimization, indexing, execution plan analysis, and database performance tuning.
• Develop data extraction, transformation, and integration workflows to support applications, analytics, and AI use cases.
• Implement relational data models and ensure data quality, consistency, integrity, and security.
• Integrate SQL Server data with Power BI, React applications, Python services, and AI-powered applications.
• Support structured data retrieval and integration with Generative AI and RAG solutions.
• Troubleshoot database performance, connectivity, and data processing issues.

Generative AI & LLM Application Development

• Design, develop, and integrate Generative AI and Large Language Model (LLM) capabilities into enterprise applications.
• Build AI-powered applications using Python and frameworks such as LangChain, LlamaIndex, or equivalent orchestration frameworks.
• Integrate LLM APIs and enterprise AI platforms, including Azure OpenAI, OpenAI APIs, Google Vertex AI, or equivalent services.
• Develop intelligent applications supporting natural-language search, document summarization, knowledge retrieval, conversational interfaces, and analytical insights.
• Implement prompt engineering, structured outputs, tool/function calling, and context management techniques.
• Develop reusable AI services and REST APIs for integration with React-based applications and enterprise platforms.
• Evaluate LLM responses for accuracy, relevance, latency, cost, and reliability.
• Implement guardrails, secure prompt handling, sensitive-data protection, and responsible AI practices.
• Troubleshoot AI application issues involving model responses, context limitations, integration failures, and performance.

Retrieval-Augmented Generation (RAG) & Vector Databases

• Design, develop, implement, and optimize Retrieval-Augmented Generation (RAG) solutions using enterprise structured and unstructured data.
• Build document ingestion, preprocessing, chunking, embedding generation, indexing, retrieval, and response generation pipelines.
• Work with vector databases such as Pinecone, Milvus, Weaviate, Chroma, FAISS, or PostgreSQL with pgvector.
• Implement semantic search, similarity search, metadata filtering, and hybrid retrieval techniques.
• Integrate vector databases with LLMs, Python services, and enterprise knowledge repositories.
• Develop enterprise knowledge assistants, document search applications, and AI-powered question-answering solutions.
• Implement embedding models, vector indexing strategies, retrieval optimization, and reranking techniques.
• Improve RAG response quality through retrieval evaluation, grounding, citation generation, and hallucination reduction.
• Integrate SQL Server and other enterprise data sources with RAG workflows where appropriate.
• Implement document-level access controls, data isolation, secure retrieval, and appropriate handling of confidential enterprise information.
• Monitor and optimize retrieval accuracy, response latency, token usage, and overall AI application performance.

Programming, Automation & API Integration

• Develop automation solutions using Python, JavaScript, TypeScript, PowerShell, and SQL.
• Build backend services and APIs using frameworks such as FastAPI, Flask, Node.js, or equivalent technologies.
• Automate data ingestion, validation, transformation, reporting, and operational workflows.
• Develop reusable scripts and components for AI applications, analytics platforms, and enterprise integrations.
• Integrate REST APIs, LLM APIs, vector databases, SQL Server, and frontend applications.
• Implement error handling, logging, exception management, and automated testing.
• Support CI/CD pipelines using GitHub Actions, Jenkins, Azure DevOps, or equivalent tools.
• Apply version control, secure coding standards, and automated deployment practices.

Analytical Problem Solving & Intelligent Solutions

• Analyze complex business and technical problems and translate requirements into scalable software and analytical solutions.
• Develop data-driven insights using SQL, Power BI, Python, and AI-assisted analytical techniques.
• Identify opportunities to apply Generative AI, semantic search, RAG, and intelligent automation to enterprise workflows.
• Perform data profiling, reconciliation, anomaly detection, and root cause analysis.
• Evaluate AI use cases based on feasibility, business value, security, accuracy, and operational impact.
• Collaborate with stakeholders to define KPIs, reporting requirements, and intelligent application capabilities.
• Communicate technical findings and analytical insights effectively to technical and non-technical audiences.

Required Experience & Technical Skills

• 3-5 years of overall IT, software engineering, application development, or data analytics experience, with hands-on experience in modern application development and AI technologies.
• Strong hands-on experience with Microsoft Power BI, including DAX, Power Query, semantic modeling, dashboard development, and Power BI Service.
• Extensive experience with MS SQL Server and T-SQL, including complex queries, stored procedures, views, database design, and performance optimization.
• Strong hands-on expertise in React.js, JavaScript, TypeScript, HTML, and CSS.
• Strong programming experience in Python, including API development, automation, data processing, and integration.
• Solid understanding of software engineering principles, data structures, algorithms, object-oriented programming, and design patterns.
• Hands-on experience developing Generative AI and LLM-powered applications.
• Practical knowledge of Retrieval-Augmented Generation (RAG) architecture, implementation, and optimization.
• Experience working with vector databases such as Pinecone, Milvus, Weaviate, Chroma, FAISS, or pgvector.
• Understanding of embeddings, semantic search, vector similarity, document chunking, retrieval strategies, and context augmentation.
• Experience with LangChain, LlamaIndex, or equivalent LLM application frameworks.
• Experience integrating LLM APIs, REST services, enterprise databases, and frontend applications.
• Strong understanding of data analytics, data modeling, data transformation, and reporting solutions.
• Experience with Git, CI/CD pipelines, automated testing, and Agile software development practices.
• Knowledge of secure application development, data privacy, authentication, authorization, and enterprise AI governance.
• Strong analytical thinking, debugging, troubleshooting, and problem-solving capabilities.
• Ability to independently design, develop, deploy, and support enterprise-grade software and AI-enabled solutions.

Preferred Qualifications

• Knowledge of data warehousing, dimensional modeling, and enterprise data governance.
• Experience building AI-powered dashboards, conversational analytics, or natural-language interfaces for enterprise data.
• Experience developing applications in large-scale, highly regulated environments.