{"id":1606283,"url":"https://alion.io/job/ups-intermediate-ai-engineer-python-rag-agentic-ai-adk-mcp-gcp-vertex-ai-ibm-watsonx","title":"Intermediate AI Engineer – Python, RAG, Agentic AI, ADK, MCP, GCP, Vertex AI, IBM Watsonx","company":{"id":37664,"name":"UPS","domain":"ups.com","url":"https://alion.io/company/ups","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Phenom","truth_index":{"grade":"B","score":84,"open_postings":1925,"ghost_share":0.205,"stale_share":0.119,"repost_share":0.407,"time_to_fill_p50_days":10,"computed_at":"2026-11-01T06:10:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chennai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":16000,"max_usd":39000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":32},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GCP","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SQL","optional":false},{"name":"Vertex AI","optional":false},{"name":"AutoGen","optional":true},{"name":"CI/CD","optional":true},{"name":"Interpretability","optional":true}],"status":"live","first_seen_at":"2026-08-15T00:00:00Z","employer_posted_date":"2026-08-15","last_verified_at":"2026-11-02T03:06:55Z","board_verified":true,"closed_at":null,"days_open":79,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":79},"description":"Before you apply to a job, select your language preference from the options available at the top right of this page.\nExplore 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.\nJob Description:\nJob Summary\nWe are seeking a highly skilled AI Engineer 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.\nThis 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.\nResponsibilities\nAgentic AI Development:\n\nDesign, build, and deploy agentic AI systems using frameworks such as LangChain, LangGraph, and related libraries.\nDevelop and deploy multi-agent systems capable of autonomous decision-making, reasoning, planning, and collaboration.\nRAG Pipelines:\n\nImplement and optimize retrieval-augmented generation (RAG) systems, ensuring agents can access and incorporate external knowledge sources for grounded, accurate responses.\nLLM Engineering:\n\nFine-tune and prompt-engineer LLMs for task-specific reasoning, planning, and dynamic adaptation.\nWork with LLM/SLM APIs, embeddings, and advanced generative AI techniques.\nEnterprise AI Platform:\n\nLead the development of enterprise-grade AI platforms integrating LLMs, RAG, embeddings, and agentic AI protocols.\nImplement and standardize Model Context Protocol (MCP) for consistent context management across models and agents.\nMLOps & Observability:\n\nEstablish and enforce best practices for MLOps, monitoring, and observability, ensuring scalable and maintainable AI solutions.\nApplied AI Prototyping:\n\nRapidly prototype, experiment, and iterate to improve AI agent capabilities.\nCollaboration & Research:\n\nParticipate in the full research cycle: literature review, data exploration, experimentation, and presentation of findings.\nCollaborate effectively with other engineers, researchers, and data scientists.\nContribute to the documentation and standardization of technical code and practices.\nRequired Education\nBachelor’s degree in Computer Science, Engineering, or a related quantitative field. Master’s or Ph.D. is a strong plus.\nRequired Experience\n5+ years overall experience in software development, data science, or machine learning.\n\n1+ year of hands-on experience developing AI applications with LLMs and systems such as retrieval-based methods, fine-tuning, or agent-based architectures.\n\n1+ year of experience with frameworks like LangChain, LlamaIndex, OpenAI, or similar tools.\n\nRequired Technical Skills\nStrong programming skills in Python and basics in SQL.\n\nExpertise with LLM/SLM APIs, embeddings, and RAG systems.\n\nExperience deploying on Google Cloud Platform (GCP) with Vertex AI, and IBM WatsonX.\n\nFamiliarity with agentic AI protocols and exposure to Agent Development Kits (ADKs).\n\nPreferred Qualifications\nExperience implementing Model Context Protocol (MCP) for agent coordination.\n\nPrior exposure to LangGraph, AutoGen, or related orchestration frameworks.\n\nKnowledge of MLOps best practices (CI/CD for ML, observability, monitoring, 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