AI/ML Engineer

Job Summary

Seeking an experienced Senior AI/ML Engineer specialized in Generative AI and Agentic Systems.

Responsibilities

• Role focuses on the end-to-end design, development, optimization, and deployment of autonomous and semi-autonomous AI agents.
• Architect multi-agent orchestration systems, design enterprise-grade Retrieval-Augmented Generation (RAG) pipelines, and implement production-ready MLOps infrastructure utilizing Google Cloud Platform (GCP) and containerized environments.
• Design, develop, and deploy autonomous and semi-autonomous AI agents utilizing leading frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, or Google's Agent Development Kit/Vertex AI Agent Builder to automate and optimize enterprise business processes.
• Architect complex multi-agent systems, establishing robust orchestration patterns, task decomposition methods, cognitive planning loops, and inter-agent communication protocols like Model Context Protocol, function-calling, and structured tool execution.
• Build, secure, and maintain integrations between AI agents and external enterprise systems, APIs (REST/GraphQL), databases, and Google Cloud services such as BigQuery, Cloud Functions, Cloud Run, and Vertex AI APIs.
• Implement advanced memory management paradigms including short-term, long-term, episodic, and semantic memory to maintain context, state, and historical execution metadata across user sessions.
• Author, test, and optimize advanced prompt templates and system instructions while establishing and maintaining reusable prompt libraries to ensure deterministic, safe, and repeatable agent behaviors.
• Evaluate, select, and fine-tune foundation models such as Gemini or other open-source/proprietary models via Vertex AI Model Garden to balance model capability, execution latency, and API inference costs.
• Design and optimize high-throughput, low-latency RAG pipelines, ensuring clean document ingestion, smart text chunking (semantic/character-based), and high-quality embedding generation.
• Integrate and manage scalable vector databases such as Vertex AI Vector Search, AlloyDB, or similar vector stores to ground AI agent responses in verified enterprise knowledge.
• Analyze, clean, and pre-process complex structured and unstructured data sources to optimize model ingestion and ensure efficient data access.
• Write, test, and maintain declarative Infrastructure as Code scripts using Terraform to provision secure GCP environments, including GKE clusters, Cloud Run services, Vertex AI endpoints, storage buckets, and networking components.
• Build and maintain CI/CD pipelines using Cloud Build, GitLab CI, or Jenkins for automated testing, container building, and seamless multi-environment deployment of agent configurations, prompt files, and backend tools.
• Package application code, agents, and dependencies into secure Docker containers, orchestrating deployments on Google Kubernetes Engine (GKE) or deploying serverless workflows via Cloud Run.
• Configure, schedule, and maintain workflow orchestrators such as Vertex AI Pipelines or Cloud Composer/Airflow to automate scheduled agent evaluation cycles, model fine-tuning, and data ingestion processes.
• Establish comprehensive evaluation metrics and testing pipelines to measure task completion rates, reasoning depth, tool-calling precision, latency, token consumption, and hallucination rates utilizing LLM-as-a-judge and Vertex AI evaluation tools.
• Implement robust input/output content filtering, moderation tools, grounding validators, prompt-injection defenses, data privacy checks, and human-in-the-loop approval gates.
• Implement production monitoring, logging, distributed tracing, and real-time alerting using Cloud Monitoring, Cloud Logging, Cloud Trace, Prometheus, Grafana, or dedicated LLM observability tools like LangSmith.
• Continually audit and optimize agent workflows, model parameters, caching strategies, and underlying infrastructure to maximize cost-efficiency and performance under high loads.
• Adhere to strict version control standards using Git, managing branching, pull requests, and code review workflows for code, prompts, Dockerfiles, and Terraform scripts.
• Partner closely with Data Scientists, Software Engineers, and business units to translate complex operational requirements into scalable production-grade AI solutions.
• Maintain comprehensive technical documentation, including system architecture diagrams, agent flowcharts, tool definitions, prompt engineering strategies, containerization guidelines, and standard operating procedures.