Description

AI Product Development

• Design and develop modular, reusable AI products and services that can be leveraged across multiple business units.
• Build scalable Retrieval-Augmented Generation (RAG) solutions utilizing structured and unstructured enterprise data.
• Develop multi-agent AI systems to automate complex business workflows and decision support processes.
• Build transcription, NLP, and LLM-powered applications for customer interaction analysis and operational intelligence.
• Translate business requirements into production-ready AI applications with a focus on usability, scalability, and business value.

Full-Stack AI Engineering

• Design and develop end-to-end AI applications across the front-end, backend, APIs, and data layers.
• Build REST APIs and microservices that integrate AI capabilities into enterprise applications.
• Develop user-facing AI features and interfaces using modern front-end technologies such as React or Angular.
• Integrate AI models, orchestration frameworks, and business workflows into cohesive production applications.

Model Engineering & AI Platforms

• Develop and fine-tune AI models using frameworks such as PyTorch, Hugging Face Transformers, LangChain, and related technologies.
• Package and deploy AI models utilizing Azure ML, Google Cloud Platform (GCP), and Databricks.
• Integrate enterprise data platforms, including Databricks, for data ingestion, feature engineering, experimentation, and continuous model improvement.
• Optimize model performance, scalability, and maintainability for enterprise production environments.

Platform Enablement & Collaboration

• Partner with Product Management, Solution Architecture, MLOps, DevOps, and Data Engineering teams to standardize AI delivery patterns.
• Develop shared libraries, APIs, templates, and reusable components that accelerate AI product development across the enterprise.
• Provide technical guidance and mentorship to engineering teams adopting reusable AI capabilities.
• Contribute to AI engineering standards, best practices, and development frameworks.

Security, Observability & Governance

• Design AI applications that meet enterprise security, compliance, and governance standards.
• Implement monitoring for model performance, application health, data drift, and usage metrics.
• Build observability into AI products to ensure reliability, transparency, and continuous improvement.
• Support responsible AI practices through monitoring, auditing, and operational governance.

REQUIRED QUALIFICATIONS

• 7+ years of software engineering experience with hands-on application development.
• Experience designing and building production AI applications utilizing Large Language Models (LLMs).
• Experience developing Retrieval-Augmented Generation (RAG) solutions.
• Experience building multi-agent AI workflows and orchestration frameworks.
• Strong Python development experience.
• Experience with AI frameworks such as LangChain, Hugging Face Transformers, or PyTorch.
• Experience developing REST APIs and microservices.
• Full-stack development experience utilizing React, Angular, or similar front-end frameworks.
• Experience deploying AI applications to Azure, Google Cloud Platform (GCP), Azure ML, Databricks, or similar cloud platforms.
• Experience integrating structured and unstructured enterprise data into AI applications.
• Understanding of software engineering best practices including testing, CI/CD, security, scalability, and observability

Key Skills

Education

Bachelor's degree