Artificial Intelligence Engineer

Stefanini; Bengaluru, Mumbai; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Stefanini in Bengaluru seeks an experienced AI Engineer to design, develop, and deploy generative AI features in production environments. You will build text generation, summarization, and conversational agents, integrating them into scalable backend systems with secure REST APIs.

Strong LLM knowledge, prompt engineering, and back-end expertise are essential for success in this role. The ideal candidate will implement RAG pipelines, agent orchestration, and tooling integration using platforms

Qualifications

• 5+ years of experience in AI engineering or related field.
• Strong background in LLM systems and prompt engineering.
• Experience building production-grade AI features and back-end integration.

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.

Skills

LLM systems

Prompt engineering

Backend development

Security best practices

Observability

Tools

Docker

Kubernetes

REST APIs

Vertex AI

Gemini API

ADK

Job description

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 modern 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.