Agent development kits jobs

Senior AI Engineer – Python, RAG, Agentic AI, ADK, MCP, GCP, Vertex AI, IBM Watsox

UPS Supply Chain Solutions · Posted Oct 7, 2025

Open

India
Full-time · Senior
5+ years

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Senior AI Engineer – Python, RAG, Agentic AI, ADK, MCP, GCP, Vertex AI, IBM Watsox

UPS Supply Chain Solutions · Posted Oct 7, 2025

Open

India
Full-time · Senior
5+ years

Apply with Referso

Intermediate AI Engineer – Python, RAG, Agentic AI, ADK, MCP, GCP, Vertex AI, IBM Watsonx

UPS Supply Chain Solutions · Posted Sep 29, 2025

Open

India
Full-time
5+ years

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Design, develop, and deploy cutting‑edge AI systems leveraging LLMs, RAG, and agentic AI architectures.

Responsibilities

Design, build, and deploy agentic AI systems using LangChain, LangGraph, and related libraries
Develop and deploy multi‑agent systems capable of autonomous decision‑making, reasoning, planning, and collaboration
Implement and optimize retrieval‑augmented generation (RAG) systems
Fine‑tune and prompt‑engineer LLMs for task‑specific reasoning, planning, and dynamic adaptation
Work with LLM/SLM APIs, embeddings, and advanced generative AI techniques

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related quantitative field
Master’s or Ph.D. is a strong plus
Nice to have: Model Context Protocol (MCP)
Nice to have: LangGraph
Nice to have: AutoGen

Skills

Python SQL LLM/SLM APIs embeddings RAG systems Google Cloud Platform (GCP)

Intermediate AI Engineer – Python, RAG, Agentic AI, ADK, MCP, GCP, Vertex AI, IBM Watsonx

UPS Supply Chain Solutions · Posted Nov 4, 2025

Open

Full-time · Mid
5+ years

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Design, build, and deploy agentic AI systems, RAG pipelines, and enterprise-grade AI platforms using LLMs and modern frameworks.

Responsibilities

Design, build, and deploy agentic AI systems using frameworks such as LangChain, LangGraph, and related libraries.
Develop and deploy multi-agent systems capable of autonomous decision-making, reasoning, planning, and collaboration.
Implement and optimize retrieval-augmented generation (RAG) systems.
Fine-tune and prompt-engineer LLMs for task-specific reasoning, planning, and dynamic adaptation.
Lead the development of enterprise-grade AI platforms integrating LLMs, RAG, embeddings, and agentic AI protocols.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related quantitative field. Master’s or Ph.D. is a strong plus.
Nice to have: Model Context Protocol (MCP)
Nice to have: LangGraph
Nice to have: AutoGen
Nice to have: MLOps best practices

Skills

Python SQL LLM/SLM APIs embeddings RAG systems

Forward Deployed Engineer (FDE) - Developer Platform

Workday · Posted Nov 6, 2026

Open

Pleasanton, California, United States
Full-time
$148K–$222K per year
5+ years

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Build, deploy, and optimize custom AI agents and agentic apps inside live customer environments on the Workday Developer Platform.

Compensation

Primary Location Base Pay Range: $148,000 USD - $222,000 USD

Responsibilities

Build, configure, test, and debug custom AI agents and agentic apps inside live enterprise tenants
Participate in technical discovery sessions with customer stakeholders
Document bugs and friction points for developer toolkit improvement
Contribute reusable components and documentation to reference architectures
Own technical workstreams within customer engagements

Requirements

Nice to have: Workday business processes
Nice to have: Workday security and permissions
Nice to have: tenant configuration
Nice to have: MCP server consumption
Nice to have: LLM-powered system testing

Skills

Python Java REST APIs LLM agentic systems

Senior AI Engineer – Python, RAG, Agentic AI, ADK, MCP, GCP, Vertex AI, IBM Watsox

UPS Supply Chain Solutions · Posted Mar 10, 2026

Open

India
Full-time · Senior
IN - TDC 1 (IN110)
5+ years

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Seeking a highly skilled AI Engineer to design, develop, and deploy cutting-edge AI systems leveraging LLMs, Chatbots, RAG, and agentic AI architectures for enterprise AI platforms.

Responsibilities

Design, build, and deploy agentic AI systems using frameworks such as LangChain, LangGraph, and related libraries.
Develop and deploy multi-agent systems capable of autonomous decision-making, reasoning, planning, and collaboration.
Implement and optimize retrieval-augmented generation (RAG) systems, ensuring agents can access and incorporate external knowledge sources for grounded, accurate responses.
Fine-tune and prompt-engineer LLMs for task-specific reasoning, planning, and dynamic adaptation.
Work with LLM/SLM APIs, embeddings, and advanced generative AI techniques.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related quantitative field. Master’s or Ph.D. is a strong plus.
Nice to have: Model Context Protocol (MCP)
Nice to have: Lang Graph
Nice to have: AutoGen
Nice to have: MLOps

Skills

Python SQL LLM/SLM APIs embeddings