AI Integration Developer

Job Description: AI Integration Developer **Department:** Information Technology / Software Development **Job Title:** AI Integration Developer **Employment Type:** Full\-Time **Experience:** Relevant experience in AI/ML development, software integration, and cloud\-based application development Job Summary We are seeking a skilled and experienced **AI Integration Developer** to design, develop, integrate, and deploy artificial intelligence solutions within existing enterprise applications and business workflows. The ideal candidate will have strong expertise in machine learning, large language models (LLMs), generative AI, Retrieval\-Augmented Generation (RAG), and cloud\-based AI services. The role involves developing intelligent applications, integrating AI models with .NET\-based systems, building scalable APIs and backend services, and implementing AI\-driven automation solutions. The candidate will also contribute to solution architecture, production deployment, model monitoring, and technical documentation while collaborating with cross\-functional teams to deliver secure, reliable, and scalable solutions. Key Responsibilities1\. AI and Machine Learning Development * Design, develop, train, validate, and optimize machine learning models using supervised and unsupervised learning techniques. * Implement classification, regression, and clustering algorithms for business and enterprise applications. * Perform feature engineering, data preprocessing, model evaluation, and performance optimization. * Develop and integrate AI\-powered applications using TensorFlow, PyTorch, and Scikit\-learn. * Monitor model performance and implement improvements to ensure accuracy, reliability, and scalability. 2\. Generative AI and LLM Integration * Develop and integrate AI\-powered applications using large language models (LLMs), including GPT and Claude. * Implement prompt engineering techniques to improve the performance and accuracy of AI\-powered solutions. * Design and develop Retrieval\-Augmented Generation (RAG) pipelines using vector databases such as Pinecone, FAISS, and Weaviate. * Build intelligent chatbots, conversational AI systems, document processing solutions, and automated knowledge retrieval applications. * Integrate generative AI capabilities into existing enterprise platforms and business workflows. 3\. Cloud\-Based AI Development and Deployment * Develop, deploy, and manage AI solutions using AWS services, including Amazon SageMaker, AWS Bedrock, AWS Lambda, Amazon S3, and IAM. * Work with AWS Nextflow and cloud\-based data processing workflows where required. * Design scalable, secure, and cost\-effective cloud architectures for AI applications. * Implement model deployment, monitoring, versioning, and scaling strategies for production environments. * Ensure cloud\-based AI applications comply with relevant security, access control, and operational requirements. 4\. .NET Application Development and API Integration * Develop and maintain backend services and RESTful APIs for integrating AI models with enterprise applications. * Integrate AI solutions into Microsoft .NET technologies, including ASP.NET Core, Blazor, MVC, Web APIs, and Entity Framework. * Design and implement secure API integrations, database connectivity, authentication mechanisms, and application services. * Work with SQL and NoSQL databases to support AI\-driven applications and data processing requirements. * Follow microservices architecture, API\-first design principles, and established software development standards. 5\. Data Engineering and Processing * Develop and maintain data preprocessing pipelines for structured and unstructured datasets. * Process, transform, and manage large datasets to support machine learning and AI applications. * Work with domain\-specific datasets, including healthcare and genomics data, where applicable. * Implement data validation, quality checks, and efficient data management practices. * Collaborate with relevant teams to ensure data availability, consistency, and suitability for AI model development. 6\. MLOps, DevOps, and Production Support * Implement MLOps practices to streamline model development, deployment, monitoring, and lifecycle management. * Use Docker and containerization technologies to package and deploy AI applications. * Work with CI/CD pipelines, Git, and automated deployment processes. * Monitor production systems, troubleshoot technical issues, and optimize application performance. * Provide technical support during critical deployments, system upgrades, and production operations. 7\. Solution Architecture and Technical Documentation * Prepare technical documentation, system architecture diagrams, API specifications, and solution design documents. * Translate business requirements into practical AI integration solutions and technical implementation plans. * Collaborate with project managers, software developers, and other stakeholders to ensure successful project delivery. * Support structured project environments, including government projects, RFP\-based engagements, and enterprise implementation frameworks. * Clearly communicate complex AI and technical concepts to both technical and non\-technical stakeholders. Required Qualifications and Skills * Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related technical field. * Strong understanding of machine learning concepts, including supervised and unsupervised learning, model training, validation, and evaluation. * Hands\-on experience with LLMs, generative AI, prompt engineering, RAG, and vector databases. * Proficiency in Python and AI/ML frameworks such as TensorFlow, PyTorch, and Scikit\-learn. * Experience developing RESTful APIs and integrating AI models with enterprise software applications. * Good knowledge of Microsoft .NET technologies, including ASP.NET Core, MVC, Blazor, Web APIs, and Entity Framework. * Practical experience with AWS cloud services, particularly SageMaker, Bedrock, Lambda, S3, and IAM. * Understanding of SQL/NoSQL databases, microservices architecture, API security, and application integration. * Knowledge of MLOps, Docker, Git, and CI/CD pipelines. * Strong analytical, problem\-solving, and troubleshooting skills. * Excellent communication, technical documentation, and teamwork abilities. Preferred Qualifications * Experience with healthcare, genomics, licensing, insurance, or other enterprise domain\-specific systems. * Experience building and deploying production\-grade AI applications and scalable cloud\-based solutions. * Familiarity with AWS Nextflow and advanced cloud\-based data processing workflows. * Exposure to enterprise system integration, government projects, and structured RFP\-based project delivery. * Experience in designing technical architectures and preparing detailed solution proposals. * Relevant professional certifications in AWS, AI/ML, cloud computing, or software development will be an advantage. Key Competencies * AI and machine learning solution development * Generative AI and intelligent automation * Enterprise application and API integration * Cloud architecture and deployment * Software engineering and system design * Analytical thinking and problem\-solving * Technical communication and documentation * Team collaboration and stakeholder management * Production support and operational reliability Additional Requirements * Ability to work collaboratively in a structured project environment. * Willingness to support production systems, critical deployments, and operational requirements when necessary. * Ability to manage multiple technical tasks and meet project deadlines. * Commitment to maintaining secure, scalable, and maintainable software solutions. Work Location: In person