The Intelligent Application / AI-ML Engineer is the architect and guardian of intelligent application solutions that incorporate AI/ML technologies using tools such as open source AI models. This role ensures the operational efficiency and continuous refinement of integrated AI/ML solutions across the organization. Responsibilities span the design, maintenance, and optimization of intelligent applications, including troubleshooting and resolving issues that arise after implementation.
The engineer will also explore new ways to embed artificial intelligence and machine learning into applications, supporting continuous improvement, learning, and augmented decision making. A key focus is building repeatable, reusable intelligent solutions for document processing, data cleansing, and similar use cases. Key Responsibilities Solution Design and Development
Design, build, and deploy intelligent application solutions that integrate AI/ML models with business workflows using modern development frameworks and open source AI models.
Develop repeatable, reusable components and services for document processing, data extraction, classification, and data cleansing.
Translate business requirements into technical designs, solution architectures, and documented standards.
Build end-to-end applications as a full stack engineer, including front-end user interfaces, backend servers and APIs, AI/ML models, databases, and business rules.
Develop and expose AI capabilities and tools through Model Context Protocol (MCP) servers so applications can use them in a consistent, reusable way.
Implement business rules and decision logic that work alongside AI models, with clear governance, testing, and auditability.
AI/ML Operations and Optimization
Manage the lifecycle of open source AI models, including deployment, versioning, monitoring, retraining, and retirement.
Monitor model performance, accuracy, and drift, and tune solutions to maintain operational efficiency over time.
Implement feedback loops (e.g., human-in-the-loop validation) to drive continuous learning and refinement.
Optimize application performance, scalability, and resource utilization.
Support and Troubleshooting
Troubleshoot and resolve post-implementation issues in open source AI models, ML pipelines, and integrated applications.
Perform root cause analysis on production incidents and implement lasting fixes.
Maintain runbooks, support documentation, and knowledge base articles.
Partner with infrastructure, security, and platform teams to maintain a stable and compliant environment.
Collaboration
Work with business analysts, process owners, data teams, and IT to deliver solutions aligned with business goals.
Communicate progress, risks, and results clearly to technical and non-technical audiences.
Requirements Required Qualifications
Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, or a related field (or equivalent experience).
Experience in intelligent application development, with hands-on experience building enterprise applications.
Working experience with open source AI models and document understanding techniques, or comparable AI/ML frameworks and tools.
Proficiency in Python and common ML libraries (e.g., scikit-learn, pandas, TensorFlow or PyTorch).
Understanding of the ML lifecycle: data preparation, training, evaluation, deployment, monitoring, and retraining.
Experience building document processing and data cleansing solutions.
Strong troubleshooting and problem-solving skills in production environments.
Solid knowledge of APIs, integrations, SQL, and data formats (JSON, XML, CSV).
Excellent written and verbal communication skills.
Full stack development experience: proficiency in Python and Java for backend services and APIs, and Angular for front-end user interfaces.
Hands-on experience with Model Context Protocol (MCP), including building or integrating MCP servers and tools.
Experience designing and working with relational and/or NoSQL databases, including schema design and query optimization.
Preferred Qualifications
Cloud, AI/ML, or software development certifications (e.g., advanced developer or solution architect credentials).
Experience with OCR, NLP, computer vision, or generative AI/LLM integration.
Familiarity with cloud platforms (Azure, AWS, or GCP) and MLOps practices.
Knowledge of Agile delivery, CI/CD, and source control (e.g., Git).
Understanding of data governance, security, and responsible AI practices.
