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

About the role

We are seeking a highly skilled AI Engineer with 5+ years overall experience in software development, data science, or machine learning to design, develop, and deploy cutting‑edge AI systems leveraging LLMs, Chatbots, Retrieval‑Augmented Generation (RAG), and agentic AI architectures. The role involves hands‑on development with LLMs, embeddings, RAG pipelines, and multi‑agent systems using modern frameworks such as LangChain, LangGraph, and LlamaIndex. The ideal candidate has experience with Vertex AI on GCP and IBM WatsonX, fine‑tuning, and Agent Development Kits (ADKs), and is excited about building scalable, production‑grade AI platforms. Responsibilities include designing agentic AI systems, developing multi‑agent systems, implementing RAG pipelines, fine‑tuning LLMs, leading enterprise‑grade AI platform development, standardizing Model Context Protocol (MCP), establishing MLOps best practices, prototyping, collaborating across teams, and contributing to documentation. Required education is a Bachelor’s degree in Computer Science, Engineering, or a related quantitative field, with Master’s or Ph.D. a strong plus. The role is permanent, full‑time, and based in India at UPS Logistics.

What you’ll do

• 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.
• Lead the development of enterprise‑grade AI platforms integrating LLMs, RAG, embeddings, and agentic AI protocols.
• Implement and standardize Model Context Protocol (MCP) for consistent context management across models and agents.
• Establish and enforce best practices for MLOps, monitoring, and observability, ensuring scalable and maintainable AI solutions.
• Rapidly prototype, experiment, and iterate to improve AI agent capabilities.
• Participate in the full research cycle: literature review, data exploration, experimentation, and presentation of findings.
• Collaborate effectively with other engineers, researchers, and data scientists.
• Contribute to the documentation and standardization of technical code and practices.

What you’ll bring

• 5+ years overall experience in software development, data science, or machine learning.
• 1+ year hands‑on experience developing AI applications with LLMs and systems such as retrieval‑based methods, fine‑tuning, or agent‑based architectures.
• 1+ year experience with frameworks like LangChain, LlamaIndex, OpenAI, or similar tools.
• Strong programming skills in Python and basics in SQL.
• Expertise with LLM/SLM APIs, embeddings, and RAG systems.
• Experience deploying on GCP with Vertex AI, and IBM WatsonX.
• Familiarity with agentic AI protocols and exposure to Agent Development Kits (ADKs).

Nice to have

• Experience implementing Model Context Protocol (MCP) for agent coordination.
• Prior exposure to LangGraph, AutoGen, or related orchestration frameworks.
• Knowledge of MLOps best practices (CI/CD for ML, observability, monitoring, scaling).
• Familiarity with responsible AI principles (safety, fairness, interpretability).
• Experience in enterprise‑scale deployments of AI‑driven platforms.
• Contributions to open‑source AI/ML projects are a plus.

Skills

Education

Bachelor