Agentic AI Engineer

We are CirrusLabs . Our vision is to become the world's most sought-after niche digital transformation company that helps customers realize value through innovation. Our mission is to co-create success with our customers, partners and community. Our goal is to enable employees to dream, grow and make things happen. We are committed to excellence. We are a dependable partner organization that delivers on commitments. We strive to maintain integrity with our employees and customers. Every action we take is driven by value. The core of who we are is through our well-knit teams and employees. You are the core of a values driven organization.

You have an entrepreneurial spirit. You enjoy working as a part of well-knit teams. You value the team over the individual. You welcome diversity at work and within the greater community. You aren't afraid to take risks. You appreciate a growth path with your leadership team that journeys how you can grow inside and outside of the organization. You thrive upon continuing education programs that your company sponsors to strengthen your skills and for you to become a thought leader ahead of the industry curve.

You are excited about creating change because your skills can help the greater good of every customer, industry and community. We are hiring a talented < Agentic AI Engineer > to join our team. If you're excited to be part of a winning team, Cirrus Labs ( http://www.cirruslabs.io ) is a great place to grow your career.

Experience: 4-6 years
Location: Bangalore
Shift Timings; 12 PM - 9 PM IST

Role Overview

We are looking for an AI Engineer specializing in Agentic AI systems and cloud-native deployment. This role focuses on building intelligent, autonomous systems using LLMs, RAG architectures, and emerging protocols like MCP, with an emphasis on scalable, production-ready implementations.

Key Responsibilities

Design and build Agentic AI systems capable of planning, reasoning, and tool usage

Develop and optimize RAG (Retrieval-Augmented Generation) pipelines for enterprise use cases

Implement and integrate Model Context Protocol (MCP) or similar frameworks for tool orchestration

Build multi-agent workflows and autonomous decision-making systems

Deploy AI applications on cloud platforms (AWS, Azure, GCP) with scalability and reliability

Develop APIs and services to integrate LLM-powered features into products

Work with vector databases and retrieval systems for efficient knowledge access

Optimize latency, cost, and performance of LLM-based applications

Implement observability, monitoring, and evaluation frameworks for AI systems

Collaborate with product and engineering teams to deliver production-grade AI solutions

Required Skills & Qualifications

4-6 years of experience in software engineering or AI engineering roles

Strong proficiency in Python and modern backend frameworks (FastAPI, Flask, etc.)

Hands-on experience with LLM frameworks such as LangChain, LlamaIndex, or similar

Strong understanding of RAG architectures, embeddings, chunking, and retrieval strategies

Experience with vector databases (Pinecone, Weaviate, FAISS, Chroma, etc.)

Experience building agentic workflows (tool use, memory, planning, orchestration)

Familiarity with MCP (Model Context Protocol) or similar tool-interaction paradigms

Experience deploying AI applications on cloud platforms (AWS/Azure/GCP)

Strong knowledge of Docker, Kubernetes, and microservices architecture

Experience designing and consuming REST APIs / async systems

Preferred Qualifications

Experience with multi-agent systems and orchestration frameworks

Familiarity with prompt engineering, evaluation, and guardrails

Knowledge of LLM observability tools (LangSmith, Weights & Biases, etc.)

Experience with streaming architectures and real-time AI systems

Exposure to security and governance in AI systems

Understanding of cost optimization strategies for LLM usage

Soft Skills

Strong problem-solving and system design skills

Ability to work in fast-evolving AI landscapes

Good communication and cross-functional collaboration