Description

You will design and implement enterprise-scale AI solutions using Google Cloud technologies and Generative AI frameworks.

This role is remote.

Responsibilities

• Define end-to-end AI/GenAI architecture for enterprise applications, establishing standards for LLM/SLM adoption and multi-agent systems.
• Architect scalable systems using commercial LLMs (Gemini, GPT, Claude) and open-source models (Mixtral, Mistral, Gemma, Phi-3).
• Design and optimize MLOps pipelines for training, deployment, and monitoring, including CI/CD strategies using GitHub Actions or GitLab CI.
• Build enterprise RAG pipelines and semantic search systems using Vertex AI Vector DB, ChromaDB, and data governance frameworks.
• Lead technical governance, architecture reviews, and mentorship for AI/ML engineers while collaborating with product and data teams.

Required Skills

• 12+ years of software engineering experience, including 7+ years focused on AI/ML and Generative AI.
• Deep expertise in Google AI ecosystem: Vertex AI, Gemini, Agent Development Kit (ADK), and AI Studio.
• Strong proficiency in Python and familiarity with Node.js for backend API development.
• Hands-on experience with LLMs, SLMs, RAG architectures, and multi-agent orchestration frameworks like LangChain, LlamaIndex, and LangGraph.
• Proven track record designing production-grade, scalable AI systems using cloud-native architecture (GCP).
• Experience with MLOps tools (LangSmith, MLflow, Weights & Biases) and model evaluation frameworks (HELM, lm-eval, RAGAS).
• Understanding of model optimization techniques including fine-tuning (LoRA, QLoRA, PEFT) and prompt engineering.

Preferred Skills

• Google Cloud Certifications (Professional ML Engineer or Cloud Architect).
• Experience with semantic kernels, intelligent automation pipelines, and hybrid/on-prem deployment environments.

Key Skills

Education

Any Graduate