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Strong PwC brand, mid-level (4-7 years), and Bangalore location increase applicant density despite niche skills.
Highly specialized agentic AI and LLM engineering narrowly fits candidates with direct ML/AI agent experience.
Multiple mandatory domain-specific skills and explicit 4-7 years requirement raise shortlisting strictness.
Design, build, and deploy autonomous multi-agent AI systems integrating large language models (LLMs) with tools and data sources, focusing on multi-step reasoning, planning, and decision-making.
Develop backend services and APIs using FastAPI, manage agent state/sessions on PostgreSQL/Redis, and implement event-driven workflows with message queues (Redis Streams, Kafka, RabbitMQ).
Implement guardrails, human-in-the-loop checkpoints, and observability (tracing, logging, KPIs) to ensure agent reliability, safety, and performance.
4-7 years of relevant work experience in AI engineering or backend development with Python.
Mandatory skills include advanced Python (async, OOP), agent frameworks (LangChain, LangGraph, AutoGen, CrewAI or equivalent), LLM APIs (OpenAI, Anthropic, HuggingFace), FastAPI, messaging systems (Redis, Kafka, RabbitMQ), and PostgreSQL.
Bachelor's degree in Technology (BE/BTech) or related fields (MCA/MTech/MBA).
Work Experience Required: 4-7 years explicitly mentioned; Notice period: Not explicitly mentioned in the JD.
Experienced in architecting and deploying autonomous AI agents with multi-agent orchestration for complex workflows.
Strong background combining AI engineering, backend microservices development, and system design operating with modern agent frameworks and LLMs.
Familiarity with observability, AI safety principles, and integration of agents with diverse data sources and external systems under compliance constraints.