Google Cloud ML Engineer

Tech Mahindra•Plano, TX

1d•$120,000 - $140,000•Onsite

About The Position

Google Cloud ML Engineer- Vertex AI & CCAI Chat Virtual Agent Expert Dallas, TX (Day1 Onsite) We seek an experienced developer to design, build, and deploy advanced conversational AI solutions on Google Cloud. You will leverage Vertex AI, CCAI, and Dialogflow to create intelligent, scalable chatbots that enhance digital customer engagement.

Requirements

6+ years in software development (4+ in NLP/NLU for chat).
Advanced Python skills; experience with ML/NLP libraries (Hugging Face, TensorFlow, PyTorch).
Proven success building conversational agents with Vertex AI and Dialogflow CX/ES.
Proficiency in GCP services and cloud-native architectures.
Solid MLOps understanding (Docker, Kubernetes, CI/CD, Git). Experience with Agent Assist functionality is a plus.

Nice To Haves

Master's/PhD in Computer Science, AI/ML, or related field. GCP certifications (e.g., Google Cloud Certified Professional ML Engineer).
Experience with GenAI frameworks (PaLM, Gemini). Integration with CRM, knowledge bases, and live agent systems.
Knowledge of security and compliance in chat AI.

Responsibilities

Experience architecting, designing, and developing scalable chat Virtual Agent frameworks using Google Vertex AI, with proficiency in Vertex AI Agent Engine and Google Agent Development Kit (ADK).
Expertise in building, customizing, and optimizing chatbots and conversational flows with Dialogflow CX/ES, including implementation of advanced intent detection, context management, slot filling, and rich fulfillment.
Demonstrated ability to leverage state-of-the-art NLP, LLM, and GenAI models on Vertex AI to enhance chatbot capabilities, such as complex intent handling, summarization, entity extraction, and response generation.
Hands-on experience building and maintaining cloud-native chat Virtual Agent solutions using GCP services including CCAI, Vertex AI, Pub/Sub, Cloud Functions, Cloud Run, Cloud Storage, and BigQuery for real-time data processing, analytics, and reporting.
Proficiency in training, fine-tuning, and deploying custom LLMs, transformer models, and Retrieval Augmented Generation (RAG) pipelines to advance chat Virtual Agent intelligence.
Solid understanding and practical application of MLOps best practices for chatbot pipelines, including automation of training, deployment, testing, monitoring, and versioning with Vertex AI Pipelines, Docker, Kubernetes, and CI/CD workflows.
Strong experience developing REST APIs, gRPC, and JSON-based communication for seamless system integration.