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

Experience : 7 Years

Notice Period : Immediate Joiner

Location : Bangalore

Shift time : 2 PM to 11 PM

Core Responsibilities

• Agentic AI & LLM Systems Design, implement, and optimize Agentic AI architectures involving planning, reasoning, memory, tool-use, and orchestration.
• Build and manage multi-agent systems for complex workflows, automation, and decision intelligence.
• Implement Retrieval-Augmented Generation (RAG) pipelines with structured and unstructured data sources.
• Integrate AI agents with enterprise APIs, databases, SaaS platforms, and internal tools .
• Develop robust prompt strategies, agent workflows, fallback mechanisms, and evaluation pipelines.
• Deploy and operate LLM-based systems with cost, latency, reliability, and safety considerations.
• Data Science & Machine Learning Build, train, evaluate, and deploy ML/DL models across NLP, structured data, time-series, recommendation, and predictive analytics.
• Perform data exploration, feature engineering, statistical analysis, and hypothesis testing .
• Design scalable training 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 Design Own the full AI product lifecycle : problem definition design development deployment monitoring iteration.
• Translate business and product requirements into scalable, modular, and maintainable AI solutions .
• Design distributed, fault-tolerant, and extensible architectures for AI platforms.
• Collaborate closely with product managers, UX, backend, frontend, and platform teams .
• Enforce engineering best practices including code quality, testing, documentation, and performance optimization .
• Multi-Cloud & Infrastructure Engineering Design, deploy, and operate AI systems across AWS, Azure, and GCP (multi-cloud or hybrid).
• Use Docker, Kubernetes, Helm , and cloud-native services for scalable deployments.
• Implement Infrastructure 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 & Reliability Ensure data security, privacy, and compliance across AI systems.
• Implement secure access control, secrets management, and encrypted data pipelines.
• Apply Responsible AI practices : bias detection, fairness, explainability, auditability.
• Design systems for high availability, disaster recovery, and fault tolerance .
• Establish governance standards for models, data, and AI agents.
• Technical Leadership & Collaboration Provide 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 & MLOps Strong hands-on experience with AWS, 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 tools
• Architecture & Distributed Systems Distributed systems and event-driven architectures
• Asynchronous processing and workflow orchestration
• Scalability, reliability, and performance engineering