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.

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

Key Responsibilities:

• Architect scalable Agentic AI solutions that support enterprise-wide automation and intelligent decision-making.
• Engineer robust LLM-powered applications capable of handling complex, multi-step reasoning tasks.
• Orchestrate multi-agent workflows using frameworks such as LangChain, CrewAI, AutoGen, or Semantic Kernel to achieve autonomous task execution.
• Automate repetitive business processes by integrating AI agents with enterprise applications, APIs, and third-party services.
• Integrate external data sources, enterprise knowledge bases, and real-time APIs to enhance agent intelligence and contextual understanding.
• Optimize LLM inference performance by implementing prompt optimization, caching strategies, token management, and response streaming techniques.
• Evaluate AI system performance using industry-standard LLM evaluation frameworks such as RAGAS, TruLens, Promptfoo, and custom evaluation metrics.
• Implement guardrails, content moderation, and responsible AI practices to ensure secure, compliant, and trustworthy AI deployments.
• Develop reusable AI components, prompt libraries, and agent templates to accelerate solution development across multiple projects.
• Monitor production AI systems for latency, hallucinations, model drift, reliability, and overall operational performance.
• Design scalable memory architectures using vector databases, semantic search, and knowledge graphs to improve long-term contextual reasoning.
• Collaborate with product managers, business stakeholders, and cross-functional engineering teams to translate business requirements into intelligent AI solutions.
• Deploy production-ready AI applications using containerization technologies such as Docker, Kubernetes, and cloud-native deployment pipelines.
• Enhance Retrieval-Augmented Generation (RAG) systems through advanced document chunking, metadata filtering, reranking, and hybrid search strategies.
• Prototype innovative AI copilots, autonomous assistants, and conversational agents tailored for enterprise business functions.
• Analyze AI workflows to identify opportunities for improving response quality, execution efficiency, scalability, and operational cost.
• Govern AI models by implementing security controls, access policies, audit logging, and compliance with enterprise AI governance standards.
• Transform traditional enterprise workflows into intelligent, autonomous agent-based solutions that improve productivity and operational efficiency.
• Benchmark different LLMs, embedding models, and agent frameworks to determine the most effective solution for specific business use cases.
• Contribute to AI research initiatives by exploring emerging technologies, agent architectures, and best practices within the Generative AI ecosystem.
  
  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