AI Engineer Generative AI

We are seeking an experienced AI Engineer with 3-6 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.

• 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.

• Bachelors or Masters degree in Computer Science Engineering or a related field or equivalent practical experience.
• 3-6 years of experience in AI/software engineering including hands-on delivery of LLM-powered applications in production.
• Strong understanding of LLM fundamentals including transformer architecture tokenization context windows embeddings sampling parameters and the trade-offs between prompting RAG and fine-tuning.
• Working knowledge of common LLM failure modes (e.g. hallucination prompt sensitivity context degradation) and corresponding mitigation strategies.
• Hands-on experience with one or more major LLM platforms such as Google Gemini OpenAI Anthropic Claude or open-source models (Hugging Face vLLM).
• Practical experience building RAG systems including chunking strategies embedding models vector databases (e.g. pgvector Pinecone Weaviate Vertex AI Vector Search) and retrieval evaluation.
• Experience implementing tool/function calling and agentic workflows using frameworks such as LangGraph LangChain Google ADK or CrewAI or through custom implementations.
• Proficiency in prompt engineering supported by structured evaluation of output quality.
• Strong programming skills in Python; experience with or similar backend technologies is a plus.
• Solid backend engineering fundamentals including REST API design microservices architecture and scalable fault-tolerant system design.
• Experience integrating AI services within cloud architectures (GCP AWS or Azure) including secure API authentication and structured JSON response handling.

• 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 ) 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.