AI Engineer – GenAI & Multi-Agent Systems

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

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

• 3–8 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.