Role: AI Engineer – GenAI & Multi-Agent Systems

Location: Bangalore

Experience: 4-9 years

Immediate Joiners Only

AI Engineer – GenAI & Multi-Agent System

Key Responsibiliti

es• Design and build multi-agent AI systems capable of planning, reasoning, and task executi

on• Develop applications using LLMs (GPT, Claude, Llama, etc.) with advanced prompt engineering and orchestrati

on• Implement Agentic workflows (planner → executor → critic → memory loop

s)• Build RAG (Retrieval-Augmented Generation) pipelines with vector databases for enterprise knowledge groundi

ng• Develop tool-using agents that integrate with APIs, databases, and enterprise syste

ms• Architect and deploy AI copilots and autonomous assistants for business workflo

ws• Optimize LLM performance using fine-tuning, prompt chaining, and caching strategi

es• Implement short-term and long-term memory mechanisms (vector stores, knowledge graph

s)• Design multi-agent collaboration protocols (hierarchical, swarm, role-based agent

s)• Deploy scalable solutions using MLOps + LLMOps practices (monitoring, evaluation, guardrail

s)• Ensure AI safety, governance, and responsible AI practic

esRequired Skills & Qualificatio

ns• Bachelor’s/Master’s in Computer Science, AI, or related fie

ld• 3–8 years experience in AI/ML with strong focus on Generative

AI• Strong Python development skil

ls• Hands-on experience wit

h:o LLMs & GenAI frameworks: OpenAI, Hugging Face Transforme

rso Agent frameworks: LangChain, AutoGen, CrewAI, Semantic Kern

elo RAG pipelines & vector DBs: FAISS, Pinecone, Weavia

te• Experience building API-driven, tool-integrated AI agen

ts• Strong understanding o

f:o Prompt engineering & prompt optimizati

ono Chain-of-thought reasoning and tool augmentati

ono Context management and token optimizati

on• Experience with cloud platforms (Azure OpenAI preferred, AWS/GCP acceptabl

e)• Knowledge of Docker, Kubernetes, CI/CD pipelin

esPreferred Qualificatio

ns• Experience building multi-agent orchestration systems with role-based coordinati

on• Exposure to agent planning algorithms (ReAct, Plan-and-Execute, Tree-of-Though

t)• Experience with LLM evaluation frameworks (RAGAS, TruLens, Promptfo

o)• Knowledge of graph-based reasoning / knowledge grap

hs• Building autonomous systems or copilots in enterprise environmen

ts• Domain experience in industrial, energy, or IoT environmen

tsKey Competenci

es• Systems thinking for designing autonomous AI architectur

es• Strong problem decomposition for agent task desi

gn• Ability to balance latency, cost, and accuracy in LLM syste

ms• Communication with business stakeholders to translate workflows into agent pipelin

es• Innovation mindset with focus on applying agentic AI in producti

onTech Stack (Modern GenAI Stac

k)• Languages: Pyth

on• Frameworks: LangChain, CrewAI, AutoGen, Semantic Kern

el• LLMs: OpenAI GPT, Azure OpenAI, Claude, Lla

ma• Vector DB: Pinecone, Weaviate, FAI

SS• Orchestration: Airflow, Prefe

ct• Deployment: Docker, Kubernet

es• Cloud: Azure AI Studio / Azure ML (preferre

d)KPIs / Success Metri

cs• Autonomous task completion rate of agen

ts• Reduction in manual workflows via AI automati

on• Latency and cost optimization of LLM pipelin

es• Accuracy and reliability of agent outpu

ts• Adoption rate of AI copilots across tea