Artificial Intelligence Engineer

Role: AI Engineer
Employment Type: Permanent with VDart Digital
Work Location: Hybrid - Bengaluru OR Pune

Note : Immediate Joiners Preferred

Role Summary

AI Engineer designs, builds, and integrates GenAI capabilities such as copilots, Retrieval Augmented Generation (RAG), and intelligent workflows into enterprise applications. The role focuses on implementing LLM integrations, prompt flows, inference pipelines, APIs, and performance-optimized AI services. AI Engineers collaborate with Architects, AI Data Scientists, TPMs, and platform teams to deliver secure, scalable, and production-ready AI solutions while adhering to enterprise engineering and governance standards.

Key Responsibilities

• Design and develop GenAI-based solutions, copilots, intelligent agents, and AI-powered workflows.
• Implement LLM integrations, prompt engineering frameworks, RAG pipelines, and inference services.
• Develop scalable, production-ready AI services, APIs, and application components.
• Build application integrations, configurations, and reusable AI solution components.
• Create and execute unit and integration tests; support automated testing practices.
• Collaborate with Architects, AI Data Scientists, TPMs, and platform teams throughout solution delivery.
• Optimize AI solution performance, reliability, security, scalability, and cost efficiency.
• Apply secure coding practices, dependency management, and enterprise AI engineering standards.
• Participate in code reviews, build automation, deployment pipelines, and operational support activities.
• Contribute to continuous improvement initiatives within GenAI delivery pods.

Education

• Bachelors or Masters degree in Computer Science, Engineering, Artificial Intelligence, Machine Learning, Data Science, Mathematics, or a related field.

Experience

• 3 to 10 years of software engineering, solution design, AI/ML, or enterprise technology experience.
• Hands-on experience with GenAI, LLMs, RAG, or AI-based solution implementation.

Deliverables

• Working software increments and AI-enabled application features
• Copilots, RAG implementations, intelligent workflows, and AI services
• AI APIs, model integration components, and inference pipelines
• Unit tests and integration test assets
• Code reviews and build artifacts
• Technical and operational documentation