The Junior AI Engineer will contribute to the development of enterprise-grade Generative AI systems and intelligent automation platforms. This role involves building Retrieval-Augmented Generation workflows, integrating Large Language Models into scalable applications, optimizing semantic search systems, and supporting cloud-native AI deployments. The position requires strong technical curiosity, structured problem-solving ability, and the capability to work on rapidly evolving AI technologies in production-focused environments.

Key Responsibilities

Develop intelligent AI workflows powered by Large Language Models and retrieval-based architectures
Build and maintain Retrieval-Augmented Generation pipelines using vector search and embedding technologies
Create semantic search systems capable of delivering context-aware and accurate AI responses
Design prompt engineering strategies for structured outputs, contextual grounding, and response optimization
Configure inference workflows and optimize AI response generation performance
Implement reasoning-oriented AI workflows using grounding methods and chain-based prompting techniques
Support AI model enhancement activities through reinforcement learning concepts and tuning strategies
Integrate AI and LLM services into scalable backend systems through APIs and microservice-based architectures
Deploy and manage AI-driven applications on Google Cloud Platform environments
Develop scalable storage and retrieval mechanisms using PostgreSQL and Firestore databases
Build and maintain RESTful APIs along with technical documentation using Swagger and Postman
Collaborate with engineering teams using GitHub-based version control and code review workflows
Research emerging AI frameworks, language models, and advanced tooling for production adoption
Improve reliability, scalability, and operational efficiency of AI-powered systems
Monitor application performance, optimize resource utilization, and support production stability initiatives
Participate in architecture discussions, technical planning, and AI innovation activities
Maintain technical records, workflow documentation, and deployment references for long-term maintainability
Contribute to continuous improvement practices focused on AI quality, performance, and automation readiness

Required Skills

Strong hands-on experience with Generative AI and LLM-powered application development
Knowledge of Retrieval-Augmented Generation workflows and vector-based information retrieval systems
Experience working with embeddings, semantic search, and contextual AI architectures
Understanding of prompt engineering techniques, grounding methods, and inference parameter optimization
Familiarity with reasoning-driven AI workflows and structured response generation approaches
Practical programming expertise in Python for AI and backend development
Experience building and integrating APIs for scalable AI services
Understanding of distributed application architecture and backend system design principles
Knowledge of PostgreSQL, Firestore, and scalable data management concepts
Hands-on exposure to Google Cloud Platform services and cloud-native deployment workflows
Familiarity with GitHub, collaborative development practices, and repository management
Experience using Postman and Swagger for API testing and technical documentation
Strong analytical thinking, debugging ability, and problem-resolution skills
Ability to manage technically complex assignments with accountability and ownership
Good communication skills and collaborative mindset for cross-functional teamwork
Adaptability to evolving AI technologies, frameworks, and production requirements

Preferred Skills

Experience with vector databases such as Pinecone, Milvus, Chroma, or similar platforms
Exposure to enterprise AI copilots, intelligent assistants, or conversational AI ecosystems
Familiarity with fine-tuning workflows and evaluation methods for Large Language Models
Understanding of MLOps practices, deployment automation, and AI lifecycle management
Awareness of CI/CD workflows, containerization, and scalable deployment strategies
Exposure to healthcare-focused AI solutions or regulated enterprise environments
Interest in AI research, emerging frameworks, and advanced language model ecosystems
Understanding of observability, monitoring, and AI system optimization practices
Familiarity with cloud-based distributed systems and intelligent workflow orchestration

Education

B.Tech / B.E. / MCA / M.Tech / BCA / B.Sc. / M.Sc. in Computer Science, Artificial Intelligence, Data Science, Information Technology, Software Engineering, Cloud Computing, or a related technical discipline from a recognized institution or university.