[Hiring] AI/Gen AI Engineer @dotSolved System Inc.

Role Description

We are hiring an AI / GenAI Engineer to build and deploy production agents on Google Vertex AI (now the Gemini Enterprise Agent Platform). You will work end-to-end — from prototyping with Gemini models and RAG, to building multi-agent workflows with the Agent Development Kit (ADK), to deploying on Agent Runtime with proper governance, evaluation, and monitoring. This is a hands-on engineering role with direct client and solution-design exposure.

Key Responsibilities

• Design, build, and deploy enterprise-grade AI agents on Vertex AI / Gemini Enterprise Agent Platform (Model Garden, Agent Builder / Agent Studio, Agent Engine / Agent Runtime, Vertex AI Pipelines, Model Registry, Endpoints).
• Develop GenAI applications using Gemini (3.x family) and other Model Garden models — prompt engineering, function calling, structured output, and model evaluation/tuning.
• Build RAG pipelines using Vertex AI Search, Vector Search, RAG Engine, and embeddings; implement grounding against enterprise data sources.
• Implement multi-agent orchestration using the Agent Development Kit (ADK), Memory Bank / Sessions, and the Agent2Agent (A2A) protocol; integrate tools via Model Context Protocol (MCP) servers.
• Productionize models and agents with solid MLOps practices: CI/CD, Vertex AI Pipelines, model/agent monitoring, observability, and cost optimization.
• Integrate AI services with enterprise systems and APIs (REST/gRPC, FastAPI) and wider GCP services (BigQuery, Cloud Run, Cloud Functions, Pub/Sub, GCS, Dataflow, GKE).
• Apply enterprise governance and security — IAM, data privacy, and responsible-AI guardrails.
• Partner with solution architects and client stakeholders to gather requirements, design solutions, and document deliverables.

Qualifications

• 4–8 years of software/ML engineering experience, with hands-on Vertex AI / Gemini Enterprise Agent Platform (or equivalent GCP AI) project experience.
• Strong Python programming; clean, production-quality code.
• Practical experience building GenAI / LLM applications — prompting, RAG, embeddings, vector databases, and model evaluation.
• Working knowledge of Google Cloud Platform core services (BigQuery, Cloud Run/Functions, GCS, IAM, Pub/Sub).
• Experience deploying and operating models/agents in production (MLOps, containerization with Docker, basic Kubernetes/GKE).
• Solid understanding of agentic AI concepts — tool use, multi-agent orchestration, memory, and agent governance.
• Strong API design and integration skills (REST/gRPC, FastAPI).
• Good communication and the ability to work directly with clients and cross-functional teams.

Good-to-Have (Preferred)

• Google Cloud certification — Professional Machine Learning Engineer or Professional Cloud Architect.
• Experience with the Agent Development Kit (ADK), A2A protocol, and MCP integrations.
• Familiarity with orchestration frameworks such as LangGraph, LangChain, or LlamaIndex.
• Exposure to other AI stacks (AWS Bedrock, Azure OpenAI, Anthropic Claude) for multi-cloud / multi-model work.
• Experience integrating AI with enterprise platforms (NetSuite, Salesforce, ERP/CRM systems).
• Background in consulting / client-facing delivery environments.

Education

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field (B.E. / B.Tech / M.E. / M.Tech / MCA). Equivalent practical experience also considered.