Opening For Senior AI Engineer | Bangalore (Karnataka)

Role - Senior AI Engineer (Multi-Agent & LLM Systems) Job Location Bangalore Experience - 6+ years of experience in AI/ML engineering & 3+ years leading complex AI initiatives Work Mode - Hybrid, 3 days office, laptop provided Interview Process - Virtual Job Summary Senior AI Engineer (Multi-Agent & LLM Systems) Quant.ai is a leading Agentic AI platform delivering production-grade AI systems for global enterprises. We work with some of the largest companies in the world across live deployments, active pilots, and strategic AI transformation initiatives. About the Role We are seeking a Senior AI Engineer to lead the architecture, evaluation, and large-scale deployment of advanced Agentic AI systems, including: Multi-agent LLM systems Retrieval-Augmented Generation (RAG) platforms Hybrid ML + GenAI systems Enterprise-grade intelligent automation platforms This is a senior technical leadership role requiring deep research expertise, strong architectural vision, and production engineering excellence. You will define the technical direction of AI systems across the organization and shape how agentic AI is deployed at enterprise scale. Role Overview As a Senior AI Engineer, you will: Architect complex multi-agent AI systems Define LLM evaluation and reliability frameworks Lead advanced Agentic AI, GenAI and ML initiatives Establish AI engineering standards across teams Own scalability, robustness, and governance of AI deployments Partner with executive leadership on AI strategy and innovation roadmap This is a high-ownership, high-influence role. Core Responsibilities Multi-Agent Systems Architecture - Design and implement multi-agent LLM orchestration frameworks Architect: o PlannerExecutor models o Tool-using agents o Memory-enabled agents o Hierarchical and collaborative agent systems Define inter-agent communication protocols Implement structured reasoning pipelines Optimize token efficiency, latency, and throughput Ensure resilience and failover strategies in agent workflows LLM Systems & RAG Architecture Design scalable RAG systems including: o Embedding strategy o Intelligent chunking frameworks o Retrieval optimization o Hybrid search architectures Implement fine-tuning and instruction tuning strategies Architect hallucination mitigation mechanisms Establish prompt versioning and governance standards Optimize inference cost and performance at scale LLM Evaluation Science & Reliability Engineering Design evaluation frameworks for: o Hallucination detection o Groundedness scoring o Faithfulness assessment o Response quality benchmarking Implement automated LLM evaluation pipelines Develop synthetic dataset generation systems Design human-in-the-loop evaluation workflows Establish model drift monitoring and agent failure detection Build observability dashboards for AI behavior and reliability Define enterprise AI governance standards Machine Learning & Predictive Systems Lead development of: o Classification and regression systems o Deep learning architectures o Anomaly detection systems o Knowledge graph reasoning engines Define experimentation frameworks and statistical rigor Oversee model validation and optimization strategies Production AI & Infrastructure Architect AI systems for enterprise-grade production deployment Define MLOps and LLMOps pipelines Deploy systems using: o AWS / GCP / Azure o Docker / Kubernetes o CI/CD pipelines Implement monitoring, logging, and observability Ensure scalability for high-volume AI workloads Define cost governance and resource optimization strategies Technical Leadership & Strategic Influence Serve as architectural authority for AI systems Mentor senior AI engineers and data scientists Conduct research and architecture reviews Translate complex business challenges into scalable AI frameworks Drive long-term AI innovation roadmap in collaboration with leadership Required Qualifications Masters or PhD in Artificial Intelligence, Machine Learning, Computer Science, or related field 6+ years of experience in AI/ML engineering 3+ years leading complex AI initiatives Solid proficiency in Python Deep expertise in: o Machine Learning algorithms o Deep Learning architectures o Transformer models o Statistical modeling o LangGraph is Must Proven track record of deploying AI systems into production Preferred Qualifications Experience building production-grade multi-agent systems Expertise in LLM orchestration frameworks (LangGraph, LangChain, etc.) Experience designing and scaling RAG pipelines Experience fine-tuning large language models Knowledge graph reasoning expertise Experience with vector databases (Pinecone, Weaviate, etc.) Experience with speech AI systems (STT/TTS) .