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