Intermediate AI Engineer – Python, RAG, Agentic AI, ADK, MCP, GCP, Vertex AI, IBM Watsonx
- Compensation
- Not disclosed
- Location
- IN - TDC 1 (IN110)
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Full time
- Experience
- 1+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 5+ years overall experience in software development, data science, or machine learning.
- 1+ year of hands-on experience developing AI applications with LLMs and systems such as retrieval-based methods, fine-tuning, or agent-based architectures.
- 1+ year of experience with frameworks like LangChain, LlamaIndex, OpenAI, or similar tools.
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.
Full description
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Explore your next opportunity at a Fortune Global 500 organization. Envision innovative possibilities, experience our rewarding culture, and work with talented teams that help you become better every day. We know what it takes to lead UPS into tomorrow—people with a unique combination of skill + passion. If you have the qualities and drive to lead yourself or teams, there are roles ready to cultivate your skills and take you to the next level.
Job Description:Job Summary
We are seeking a highly skilled AI Engineer with 5+ years of overall experience in software development, Data Science, or Machine Learning to design, develop, and deploy cutting-edge AI systems leveraging Large Language Models (LLMs), Chatbots, Retrieval-Augmented Generation (RAG), and agentic AI architectures.
This role involves hands-on development with LLMs, embeddings, RAG pipelines, and multi-agent systems using modern frameworks like 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
- Agentic AI Development:
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.
- RAG Pipelines:
Implement and optimize retrieval-augmented generation (RAG) systems, ensuring agents can access and incorporate external knowledge sources for grounded, accurate responses.
- LLM Engineering:
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.
- Enterprise AI Platform:
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.
- MLOps & Observability:
Establish and enforce best practices for MLOps, monitoring, and observability, ensuring scalable and maintainable AI solutions.