AI/ML Architect
Free account needed to apply or save. It takes a minute, and we'll bring you right back to this job.
Key details
- Location
- India
- Work style
- Hybrid
- Level
- Senior
- Job type
- Full-time
- Experience
- 3–6 yrs
- Function
- Software engineering
- Focus area
- AI / ML
- Posted
- 2d ago
Skills
About the role
-
Seeking an experienced Senior AI/ML Engineer specialized in Generative AI and Agentic Systems.
-
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.
Get jobs like this as soon as they're posted
JobSuited checks company career sites all day and ranks every opening against your profile. Free to join.