Senior / Lead Agentic AI & Data Science Engineer (Product Engineering)

We areCirrusLabs. Our vision is to become the world's most sought-after niche digital transformation company that helps customers realize value through innovation. Our mission is to co-create success with our customers, partners and community. Our goal is to enable employees to dream, grow and make things happen. We are committed to excellence. We are a dependable partner organization that delivers on commitments. We strive to maintain integrity with our employees and customers. Every action we take is driven by value. The core of who we are is through our well-knit teams and employees. You are the core of a values driven organization.You have an entrepreneurial spirit. You enjoy working as a part of well-knit teams. You value the team over the individual. You welcome diversity at work and within the greater community. You aren't afraid to take risks. You appreciate a growth path with your leadership team that journeys how you can grow inside and outside of the organization. You thrive upon continuing education programs that your company sponsors to strengthen your skills and for you to become a thought leader ahead of the industry curve.You are excited about creating change because your skills can help the greater good of every customer, industry and community. We are hiring a talentedSenior / Lead Agentic AI & Data Science Engineer (Product Engineering)> to join our team. If you're excited to be part of a winning team, CirrusLabs (is a great place to grow your career.Experience - 7-10 years Location - Bengaluru Work Timings - 2pm - 11pm ISTExperience7–10 years total experiencein Data Science, AI/ML Engineering, and Product Engineering Strong hands-on experiencein building, deploying, and scalingAgentic AI systemsin production Role Summary We are looking for aSenior Agentic AI & Data Science Engineerwith adeep product engineering backgroundto architect, develop, deploy, and operateproduction-grade AI systems. The role requiresend-to-end ownershipof AI products—coveringagent design, ML modeling, system architecture, MLOps, multi-cloud deployment, security, and scalability. The ideal candidate combinesstrong AI research intuitionwithreal-world engineering excellence.Core ResponsibilitiesAgentic AI & LLM SystemsDesign, implement, and optimizeAgentic AI architecturesinvolving planning, reasoning, memory, tool-use, and orchestration. Build and managemulti-agent systemsfor complex workflows, automation, and decision intelligence. ImplementRetrieval-Augmented Generation (RAG)pipelines with structured and unstructured data sources. Integrate AI agents withenterprise APIs, databases, SaaS platforms, and internal tools. Develop robust prompt strategies, agent workflows, fallback mechanisms, and evaluation pipelines. Deploy and operateLLM-based systemswith cost, latency, reliability, and safety considerations.Data Science & Machine LearningBuild, train, evaluate, and deployML/DL modelsacross NLP, structured data, time-series, recommendation, and predictive analytics. Performdata exploration, feature engineering, statistical analysis, and hypothesis testing. Design scalabletraining pipelines, experiment tracking, and model versioning. Monitor model performance, drift, bias, and data quality in production environments. Apply explainability and interpretability techniques where required.Product Engineering & System DesignOwn thefull AI product lifecycle: problem definition → design → development → deployment → monitoring → iteration. Translate business and product requirements intoscalable, modular, and maintainable AI solutions. Designdistributed, fault-tolerant, and extensible architecturesfor AI platforms. Collaborate closely withproduct managers, UX, backend, frontend, and platform teams. Enforce engineering best practices includingcode quality, testing, documentation, and performance optimization.Multi-Cloud & Infrastructure EngineeringDesign, deploy, and operate AI systems acrossAWS, Azure, and GCP(multi-cloud or hybrid). UseDocker, Kubernetes, Helm, and cloud-native services for scalable deployments. ImplementInfrastructure as Code (IaC)using Terraform / CloudFormation. Leverage managed AI/ML services where appropriate (SageMaker, Vertex AI, Azure ML). Optimize cloud resource utilization and cost across environments.Security, Governance & ReliabilityEnsuredata security, privacy, and complianceacross AI systems. Implement secure access control, secrets management, and encrypted data pipelines. ApplyResponsible AI practices: bias detection, fairness, explainability, auditability. Design systems forhigh availability, disaster recovery, and fault tolerance. Establish governance standards for models, data, and AI agents.Technical Leadership & CollaborationProvide technical guidance and mentorship to junior engineers and data scientists. Lead architecture discussions, technical reviews, and best-practice adoption. Drive innovation in AI/Agentic systems aligned with product and business goals. Communicate complex technical concepts clearly to both technical and non-technical stakeholders.Cloud, DevOps & MLOpsStrong hands-on experience withAWS, Azure, and/or GCP(at least two preferred) Docker, Kubernetes, Helm CI/CD: GitHub Actions, GitLab CI, Jenkins MLOps tools:MLflow, Kubeflow, cloud-native ML platforms Monitoring and observability toolsArchitecture & Distributed SystemsDistributed systems and event-driven architectures Asynchronous processing and workflow orchestration Scalability, reliability, and performance engineering

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