AI engineer

Location:-Bangalore

Experience:-4 to 9 Years

Hybrid Mode

AI Engineer GenAI & Multi-Agent Systems

Role Overview

We are looking for a highly skilled AI Engineer specializing in Generative AI and Multi-Agent Systems to design and deploy intelligent, autonomous solutions. This role focuses on building LLM-powered, agent-driven architectures that can reason, collaborate, and execute complex workflows across enterprise systems.

You will work on cutting-edge Agentic AI frameworks, enabling systems that go beyond prediction to decision-making, orchestration, and autonomous execution.

Key Responsibilities

Design and build multi-agent AI systems capable of planning, reasoning, and task execution

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

Implement Agentic workflows (planner executor critic memory loops)

Build RAG (Retrieval-Augmented Generation) pipelines with vector databases for enterprise knowledge grounding

Develop tool-using agents that integrate with APIs, databases, and enterprise systems

Architect and deploy AI copilots and autonomous assistants for business workflows

Optimize LLM performance using fine-tuning, prompt chaining, and caching strategies

Implement short-term and long-term memory mechanisms (vector stores, knowledge graphs)

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

Deploy scalable solutions using MLOps + LLMOps practices (monitoring, evaluation, guardrails)

Ensure AI safety, governance, and responsible AI practices

Required Skills & Qualifications

Bachelors/Masters in Computer Science, AI, or related field

38 years experience in AI/ML with strong focus on Generative AI

Strong Python development skills

Hands-on experience with:

o LLMs & GenAI frameworks: OpenAI, Hugging Face Transformers

o Agent frameworks: LangChain, AutoGen, CrewAI, Semantic Kernel

o RAG pipelines & vector DBs: FAISS, Pinecone, Weaviate

Experience building API-driven, tool-integrated AI agents

Strong understanding of:

o Prompt engineering & prompt optimization

o Chain-of-thought reasoning and tool augmentation

o Context management and token optimization

Experience with cloud platforms (Azure OpenAI preferred, AWS/GCP acceptable)

Knowledge of Docker, Kubernetes, CI/CD pipelines

Preferred Qualifications

Experience building multi-agent orchestration systems with role-based coordination

Exposure to agent planning algorithms (ReAct, Plan-and-Execute, Tree-of-Thought)

Experience with LLM evaluation frameworks (RAGAS, TruLens, Promptfoo)

Knowledge of graph-based reasoning / knowledge graphs

Building autonomous systems or copilots in enterprise environments

Domain experience in industrial, energy, or IoT environments

Key Competencies

Systems thinking for designing autonomous AI architectures

Strong problem decomposition for agent task design

Ability to balance latency, cost, and accuracy in LLM systems

Communication with business stakeholders to translate workflows into agent pipelines

Innovation mindset with focus on applying agentic AI in production

Tech Stack (Modern GenAI Stack)

Languages: Python

Frameworks: LangChain, CrewAI, AutoGen, Semantic Kernel

LLMs: OpenAI GPT, Azure OpenAI, Claude, Llama

Vector DB: Pinecone, Weaviate, FAISS

Orchestration: Airflow, Prefect

Deployment: Docker, Kubernetes

Cloud: Azure AI Studio / Azure ML (preferred)

KPIs / Success Metrics

Autonomous task completion rate of agents

Reduction in manual workflows via AI automation

Latency and cost optimization of LLM pipelines

Accuracy and reliability of agent outputs

Adoption rate of AI copilots across teams