Campus-Competent

Job Summary

Demand Unit Name: Agentic AI Engineer II – Agent Orchestration & RAG Workflows

Band : E2.1

Target : Engineering Graduates from Tier 1 Institutions (IIT, NIT, DTU)

Purpose of the Role:

The Agentic AI Engineer II will contribute to the design, development, and optimization of agentic AI systems, multi-agent orchestration frameworks, and advanced Retrieval-Augmented Generation (RAG) workflows. The role focuses on building intelligent, autonomous agents capable of reasoning, planning, and interacting across enterprise systems while ensuring robust performance, accuracy, and scalability.
As high-potential engineers from premier institutions, they will work closely with senior architects and AI teams to deliver next-generation AI capabilities for real-world enterprise applications.

Key Responsibilities

Responsibilities:

• Develop and enhance agentic workflows using LLM-based agents, decision-making loops, and orchestration frameworks.

• Implement and optimize RAG pipelines including document ingestion, chunking strategies, embedding generation, vector search, and retrieval ranking.

• Build and maintain reusable components for agent orchestration frameworks (state management, tool invocation, memory modules, guardrails).

• Assist in constructing multi-agent systems that collaborate, negotiate, and coordinate tasks for complex enterprise workflows.

• Work with senior engineers to integrate agents with APIs, microservices, and enterprise platforms for end-to-end automation.

• Contribute to experimentation, evaluation, and benchmarking of agent behavior, retrieval quality, and task success rates.

• Participate in full lifecycle engineering: design discussions, code reviews, testing, and documentation.

• Troubleshoot issues, analyze logs and outputs, and propose improvements to reliability and performance.

• Continuously learn emerging AI techniques, frameworks, and best practices to elevate solution maturity.

Skill Requirements

Technical Skills:

Must Have :

• Strong programming skills in Python

• Strong fundamentals in algorithms, data structures, distributed systems, and software engineering.

• Solid understanding of LLMs, prompt engineering concepts, and reasoning frameworks.

• Familiarity with modern AI/ML libraries (Py Torch, Tensor Flow).

• Exposure to LLM frameworks such as Lang Chain, Llama Index, or similar orchestration tools.

• Understanding of vector databases (FAISS, Pinecone, Milvus, Weaviate) and embedding models.

• Basic experience with REST APIs, microservices, and event-driven systems.

• Familiarity with retrieval systems, text processing, and information extraction techniques.

• Understanding of cloud environments (Azure/AWS/GCP) and containerization basics (Docker).

Other Requirements

Desirable :

• Exposure to autonomous agent frameworks (e.g., Re Act, Reflexion, Auto Gen, CrewAI).

• Knowledge of RLHF concepts, tool-using agents, and planning algorithms.

• Experience in building small AI projects, hackathon prototypes, or research Po Cs.

• Familiarity with CI/CD, Kubernetes, MLflow, experiment tracking, or observability tools.

• Understanding of advanced RAG enhancements (rerankers, hybrid search, graph-based retrieval, query rewriting).

Behavioral Competencies :

• Strong analytical thinking, curiosity, and eagerness to explore emerging AI technologies.

• Ability to learn rapidly and apply concepts to real-world engineering problems.

• Collaborative mindset with openness to feedback, mentorship, and peer learning.

• High sense of ownership and responsibility for assigned tasks.

• Clear communication and documentation skills.

• Adaptability and resilience in fast-paced, evolving project environments.

• Passion for innovation, research, and continuous improvement.