Agentic Solution Design & Development

• Build and deploy Gemini-powered agentic applications using Vertex AI, ADK, and Lang frameworks.
• Design multi-step, context-aware agent workflows, including memory, grounding, tool integrations, connectors, and decision logic.
• Integrate agents with S&P data platforms, APIs, enterprise systems, and operational workflows.
• Design and implement MCP-based integrations to enable secure, governed interoperability between agents and internal tools/services.
• Build and prototype Agent-to-Agent (A2A) interaction patterns using appropriate orchestration frameworks for collaborative, multi-agent workflows.
• Convert business problems into agentic blueprints, reusable patterns, and repeatable components.

Gemini Enterprise Enablement

• Use the full Gemini Enterprise stack—models, agent platform, OOTB agents, connectors, governance controls, evaluations—to deliver enterprise-grade solutions.
• Support DTS and business teams in adopting Gemini capabilities through enablement assets, starter kits, demos, and best practices.
• Contribute to standards and safe-use guidelines for applying Gemini tools across the organization.

Observability, Safety & Governance

• Implement monitoring, evaluation, telemetry, drift detection, and performance baselines for agentic workflows.
• Establish observability patterns for A2A interactions, behavior tracing, and dependency visibility across multi-agent systems.
• Ensure alignment with DTS governance, security, and responsible AI frameworks.
• Collaborate with governance functions to define thresholds, guardrails, and operating practices for agent behavior, including MCP-mediated tool interactions.

Collaboration & Delivery

• Partner with engineering, data, research, product, and operations teams to deliver high-value agentic use cases.
• Communicate clearly with stakeholders across different levels, translating technical concepts into actionable workflows.
• Contribute to cross-functional delivery initiatives where agentic capabilities drive measurable improvements in efficiency, quality, or decision-making.

Qualifications

Technical Expertise

• 7+ years of overall experience in AI/ML, with strong hands-on expertise in LLMs, generative AI, and applied machine learning.
• Experience working with Google Cloud’s AI/ML stack, including Vertex AI for model deployment, fine-tuning, embeddings, and pipelines.
• Exposure to or familiarity with the Gemini Enterprise ecosystem (models, agent platform, OOTB agents, connectors, governance)
• Strong programming proficiency in Python, with experience building prototypes and production-ready AI/LLM applications.
• Practical experience with Lang frameworks such as LangChain, LangGraph for multi-step reasoning and agentic orchestration.
• Deep understanding of LLM system design, including prompt engineering, context engineering, RAG architectures, memory design, grounding, and tool calling.
• Experience integrating LLM systems with enterprise tools using Model Context Protocol (MCP) or similar interoperability patterns.
• Familiarity with LLM evaluation and observability, including performance monitoring, drift detection, cost analysis, safety checks, and behavioral tracing.