Description - ExternalJob Description: AI Engineer (1-4 Years Overall Experience)
Location: Bengaluru
Employment Type: Full-Time
Role Purpose
The AI Engineer will design, develop and integrate AI-enabled capabilities into enterprise and manufacturing applications. The role will focus on Generative AI, intelligent automation, data integration and AI-assisted software engineering while building strong foundations in production-grade AI engineering.
The engineer will work with senior engineers and architects to convert manufacturing and business use cases into scalable AI solutions.
Key Responsibilities1. Generative AI & Agentic Solutions- Develop applications using LLMs and Generative AI.
- Build RAG-based solutions using enterprise documents and structured/unstructured data.
- Develop AI agents and tool-based workflows for engineering and business processes.
- Implement prompt engineering, structured outputs and function/tool calling.
- Evaluate AI responses for accuracy, relevance and reliability.
- Develop APIs and backend services using Python and FastAPI.
- Integrate AI capabilities into existing enterprise applications.
- Build reusable AI services/components rather than standalone prototypes.
- Develop proof-of-concepts and mature them toward production solutions.
- Write clean, modular and testable code.
- Connect AI solutions with enterprise databases, APIs, documents and engineering systems.
- Implement document ingestion, chunking, embedding and retrieval pipelines.
- Work with SQL, REST APIs, JSON and structured/unstructured datasets.
- Exposure to vector databases/search technologies such as Azure AI Search, PostgreSQL/pgvector or equivalent.
- Use AI coding assistants effectively across development, testing and documentation.
- Build automated unit and integration tests.
- Participate in code reviews and engineering quality practices.
- Work with Git, CI/CD, containers and DevSecOps pipelines.
- Contribute to reusable engineering accelerators and automation.
Work with domain experts to develop solutions such as:
- Engineering knowledge assistants
- Manufacturing/planning copilots
- Intelligent document processing
- Automated engineering workflows
- Semantic enterprise search
- AI-assisted validation and testing
- Data-driven recommendations
- Agent-based process automation
Must-have
- Bachelor's/Master's degree in Computer Science, AI, Data Science, Information Technology or related engineering discipline
- Strong programming fundamentals in Python
- Understanding of REST APIs and backend application development
- Fundamentals of Generative AI and LLMs
- Understanding of RAG, embeddings and vector search
- Working knowledge of SQL and data handling
- Git and modern software-development practices
- Strong analytical and problem-solving skills
Good-to-have
- Azure AI / cloud services
- FastAPI
- Azure OpenAI or equivalent LLM platforms
- Azure AI Search
- LangChain / Semantic Kernel or similar orchestration frameworks
- Docker / Kubernetes
- CI/CD and DevSecOps
- React/Angular knowledge
- ML/MLflow/MLOps fundamentals
- Knowledge graphs
- Agentic AI / multi-agent concepts
Description - ExternalJob Description: AI Engineer (1-4 Years Overall Experience)
Location: Bengaluru
Employment Type: Full-Time
Role Purpose
The AI Engineer will design, develop and integrate AI-enabled capabilities into enterprise and manufacturing applications. The role will focus on Generative AI, intelligent automation, data integration and AI-assisted software engineering while building strong foundations in production-grade AI engineering.
The engineer will work with senior engineers and architects to convert manufacturing and business use cases into scalable AI solutions.
Key Responsibilities1. Generative AI & Agentic Solutions- Develop applications using LLMs and Generative AI.
- Build RAG-based solutions using enterprise documents and structured/unstructured data.
- Develop AI agents and tool-based workflows for engineering and business processes.
- Implement prompt engineering, structured outputs and function/tool calling.
- Evaluate AI responses for accuracy, relevance and reliability.
- Develop APIs and backend services using Python and FastAPI.
- Integrate AI capabilities into existing enterprise applications.
- Build reusable AI services/components rather than standalone prototypes.
- Develop proof-of-concepts and mature them toward production solutions.
- Write clean, modular and testable code.
- Connect AI solutions with enterprise databases, APIs, documents and engineering systems.
- Implement document ingestion, chunking, embedding and retrieval pipelines.
- Work with SQL, REST APIs, JSON and structured/unstructured datasets.
- Exposure to vector databases/search technologies such as Azure AI Search, PostgreSQL/pgvector or equivalent.
- Use AI coding assistants effectively across development, testing and documentation.
- Build automated unit and integration tests.
- Participate in code reviews and engineering quality practices.
- Work with Git, CI/CD, containers and DevSecOps pipelines.
- Contribute to reusable engineering accelerators and automation.
Work with domain experts to develop solutions such as:
- Engineering knowledge assistants
- Manufacturing/planning copilots
- Intelligent document processing
- Automated engineering workflows
- Semantic enterprise search
- AI-assisted validation and testing
- Data-driven recommendations
- Agent-based process automation
Must-have
- Bachelor's/Master's degree in Computer Science, AI, Data Science, Information Technology or related engineering discipline
- Strong programming fundamentals in Python
- Understanding of REST APIs and backend application development
- Fundamentals of Generative AI and LLMs
- Understanding of RAG, embeddings and vector search
- Working knowledge of SQL and data handling
- Git and modern software-development practices
- Strong analytical and problem-solving skills
Good-to-have
- Azure AI / cloud services
- FastAPI
- Azure OpenAI or equivalent LLM platforms
- Azure AI Search
- LangChain / Semantic Kernel or similar orchestration frameworks
- Docker / Kubernetes
- CI/CD and DevSecOps
- React/Angular knowledge
- ML/MLflow/MLOps fundamentals
- Knowledge graphs
- Agentic AI / multi-agent concepts
