Generative AI Engineer – GCP

• Integrate commercial and open-source LLM APIs into enterprise applications and business workflows.
• Design and develop agentic AI workflows using orchestration frameworks such as LangChain and Google ADK.
• Build multi-step AI workflows supporting reasoning, tool usage, workflow orchestration, and autonomous decision-making.
• Develop and optimize prompt engineering strategies to deliver high-quality, reliable, consistent, and secure model outputs.
• Implement and manage Model Context Protocol (MCP) integrations for AI applications and agents.
• Design and develop Retrieval-Augmented Generation (RAG) pipelines using embeddings, vector databases, semantic search, and contextual retrieval.
• Work with transformer-based architectures and modern NLP techniques, including models such as BERT.
• Design highly available, scalable, secure, and resilient cloud-native architectures for enterprise AI workloads.
• Develop and deploy AI solutions using Google Cloud Platform (GCP), with hands-on experience across Vertex AI, BigQuery, Cloud Storage, Cloud Functions, and Cloud Run.
• Develop Python-based applications following sound software engineering principles, including clean coding, version control, testing, debugging, and maintainability.
• Implement AI security, data privacy, governance, and responsible AI practices across AI solutions.
• Design and implement AI Guardrails to manage model safety, reliability, data protection, and policy compliance.
• Collaborate with cross-functional teams, architects, data scientists, and business stakeholders to translate business requirements into scalable AI solutions.
• Troubleshoot, optimize, and continuously improve AI applications for performance, scalability, cost, and reliability.
• Stay current with emerging developments in Generative AI, LLMs, agentic AI, RAG, AI frameworks, and cloud technologies.

What You Need

Required Skills

• 5–7 years of professional experience in software engineering, AI/ML engineering, or a related field.
• Advanced proficiency in Python and strong software engineering fundamentals.
• Hands-on experience with Generative AI and LLM application development.
• Experience integrating commercial and/or open-source LLM APIs into enterprise applications.
• Strong experience designing and developing AI agents and agentic workflows.
• Hands-on experience with LangChain or similar AI orchestration frameworks.
• Experience with Google ADK and/or equivalent agent development frameworks.
• Experience implementing and managing Model Context Protocol (MCP) integrations.
• Strong understanding of transformer architectures, LLMs, embeddings, NLP, and semantic search.
• Hands-on experience building RAG pipelines using vector databases and embedding models.
• Strong hands-on experience with Google Cloud Platform (GCP).
• Practical experience with Vertex AI, BigQuery, Cloud Storage, Cloud Functions, and/or Cloud Run.
• Experience implementing AI security, governance, privacy, and responsible AI practices.
• Development experience with AI Guardrails is required.
• Strong understanding of software development lifecycle, version control, testing, debugging, and deployment practices.
  • Strong analytical, problem-solving, communication, and collaboration skills.