We are seeking an experienced AI Engineer with 5+years of hands-on experience designing, developing, and deploying generative AI applications in production environments. The candidate will be responsible for building intelligent, AI-powered features - including text generation, summarization, conversational AI, and agentic workflows - and integrating them securely into scalable, cloud-based backend systems.The role requires a strong foundation in large language model (LLM) systems, including prompt engineering, retrieval-augmented generation (RAG), agent orchestration, and output evaluation, combined with solid backend development expertise. Experience with the Google AI ecosystem (Gemini API, Vertex AI, Agent Development Kit) is an advantage; candidates with equivalent experience on other major LLM platforms are encouraged to apply.Key Responsibilities

- Design, develop, and deploy generative AI features such as text generation, summarization, conversational assistants, and multi-step agentic workflows.
- Architect and implement retrieval-augmented generation (RAG) pipelines, covering document ingestion, chunking, embeddings, vector store integration, retrieval and reranking, and grounding quality assessment.
- Develop agentic systems using tool/function calling, structured outputs, and orchestration patterns, incorporating appropriate guardrails, fallback mechanisms, and human-in-the-loop controls.
- Establish and maintain prompt engineering standards, including prompt versioning, structured output schemas, and data-driven optimization of response quality and accuracy.
- Build evaluation frameworks for LLM outputs, including curated test datasets,



automated evaluations, regression testing, and monitoring for hallucination and grounding quality.
- Integrate AI services into backend applications through well-designed REST APIs and microservices, with robust handling of structured JSON responses, streaming, retries, and error states.
- Implement secure API authentication and access management for AI services, including API key management, OAuth 2.0, IAM, secrets handling, and safeguards against prompt injection and data leakage.
- Monitor and optimize production performance across response latency, token cost, throughput, and output quality, supported by appropriate observability and tracing.
- Collaborate with product managers, data engineers, and application developers to embed AI capabilities into business applications while ensuring security, reliability, and compliance.

Preferred Qualifications

- Direct experience with the Google AI ecosystem, including Gemini API, Vertex AI, Google Agent Development Kit (ADK), or Google Antigravity.
- Experience with the Model Context Protocol (MCP) or building tool integrations for agentic systems.
- Exposure to model fine-tuning (e.g., LoRA/PEFT, instruction tuning) and model serving.
- Familiarity with LLM observability and evaluation tooling (e.g., LangSmith, Langfuse, Vertex AI Evaluation).
- Experience with contemporary front-end frameworks (React, Angular, or Vue.js) for building responsive, AI-driven user interfaces.
- Experience with containerization and orchestration (Docker, Kubernetes) and CI/CD practices.
- Familiarity with responsible AI practices, including content safety, PII handling, and compliance considerations.

📌 Artificial Intelligence Engineer (Bengaluru)
🏢 Stefanini
📍 Bengaluru