Generative AI Engineer

We’re seeking a Gen AI Specialist with deep hands‑on experience on Google Cloud Platform and Google’s Gemini models. In this role, you’ll lead the design, prototyping, and deployment of generative‑AI solutions—building conversational agents, content‑generation pipelines, and embedding advanced LLM capabilities into customer applications. Core Responsibilities • Model Development & Fine‑Tuning: • Design, fine‑tune, and evaluate Gemini-based models (and other LLMs as needed) for use cases like chatbots, document summarization, and code generation. • Develop custom training workflows on Vertex AI, leveraging techniques such as parameter-efficient fine‑tuning and retrieval‑augmented generation (RAG). • Gen AI Solution Architecture: • Define end‑to‑end architectures that integrate GCP services (Vertex AI, Pub/Sub, Dataflow, BigQuery) with Gemini inference endpoints. • Implement scalable, secure pipelines for data ingestion, prompt orchestration, and model serving. • Prototype & Proof‑of‑Concepts: • Rapidly build and iterate PoCs to demonstrate Gen AI capabilities to customers. • Document performance benchmarks, cost estimates, and limitations. • Application Integration: • Embed generative‑AI functionality into web, mobile, or backend services using containers (Kubernetes/GKE) and CI/CD pipelines. • Collaboration & Best Practices: • Partner with data engineers, architects, and product teams to ensure alignment with broader data strategy. • Establish guidelines for prompt engineering, model governance, and monitoring. • Customer Engagement: • Lead technical workshops, requirements‑gathering sessions, and solution demonstrations. • Translate complex AI concepts into business value for stakeholders. Required Qualifications • Experience: 5–8 years in AI/ML roles, with at least 2 years focused on generative‑AI projects. • Technical Skills: • Gen AI & LLMs: Hands‑on with Google Gemini (and familiarity with other LLMs such as GPT‑4, Anthropic Claude, etc.). • GCP Services: Deep expertise in Vertex AI (Training & Prediction), Dataflow, Pub/Sub, BigQuery, Cloud Storage. • Programming: Python (incl. ML libraries: TensorFlow, PyTorch), SQL. • MLOps: Experience with Kubernetes/GKE, CI/CD (Cloud Build, GitOps), and Infrastructure‑as‑Code (Terraform). • Must have experience building Agents • Education: Bachelor’s degree in Computer Science, Engineering, Data Science, or related field. • Soft Skills: Excellent verbal and written communication; proven ability to engage non‑technical stakeholders. Preferred Qualifications • Certifications: Google Cloud Professional Machine Learning Engineer or Professional Data Engineer. • Additional Tools: Familiarity with RAG frameworks (e.g., LangChain), vector databases (e.g., Pinecone, Vertex Matching Engine). • Domain Experience: Prior work in customer‑facing AI product development or in industries such as utilities, finance, or logistics.