Solutions Architect - Presales | Elastic

Job description

It's fun to work in a company where people truly BELIEVE in what they're doing! Job Description: Job Role : As a Presales Solution Architect, you will act as a trusted technical advisor to customers, partners, sales teams, and internal stakeholders. The role requires strong expertise across the Elastic platform, including Enterprise Search, Observability, and Security solutions. You will be responsible for translating customer business challenges into scalable Elastic-based solutions, leading technical discovery, architecture discussions, demonstrations, proof-of-concepts (PoCs), and supporting successful adoption of Elastic technologies. Responsibilities : Serve as the technical Subject Matter Expert (SME) for Elastic Enterprise Solutions covering Search, Observability, and Security. Engage with customers, partners, and sales teams to understand business requirements, technical challenges, and solution objectives. Design and architect Elastic solutions aligned to customer use cases, including: Enterprise Search and AI-powered search experiences Application, infrastructure, and cloud observability Logging, monitoring, APM, tracing, metrics, and OpenTelemetry-based solutions. SIEM, threat detection, security analytics, endpoint security, and incident investigation workflows Conduct technical discovery workshops, solution presentations, whiteboarding sessions, and executive briefings. Develop solution architectures, technical proposals, sizing recommendations, and implementation approaches. Lead Proof-of-Concepts (PoCs), product demonstrations, and technical validations. Respond to RFPs/RFIs by providing accurate technical and business-aligned responses. Collaborate with Sales, Product, Marketing, and Engineering teams to drive pipeline growth and successful customer outcomes. Build competitive positioning against alternative search, monitoring, observability, and security platforms. Support partner enablement activities, technical training, and knowledge-sharing programs. Mentor junior architects and presales engineers on Elastic technologies and consultative selling practices. Support partner to Secure, maintain, and upgrade Elasticsearch clusters. Design and implement strategies for Elasticsearch performance optimization, scalability, and efficient data utilization. Strong expertise in data onboarding, indexing lifecycle/tier management, and performance tuning. Can develop and optimize ingestion pipelines, including advanced Grok parsing, event processing, and indexing strategies. Support partner to build geospatial search, aggregation, and visualization solutions. Manage structured and unstructured data ingestion workflows. Technical Skills : Strong hands-on experience with Elastic Stack components: Elasticsearch Kibana Elastic Agent / Beats Logstash Fleet Management Experience designing and implementing enterprise-scale Elastic deployments. Strong understanding of: Data ingestion, indexing, querying, dashboards, visualization, and analytics Search architecture and relevance tuning Observability concepts including logs, metrics, traces, APM, SRE practices Security operations, SIEM workflows, threat hunting, detection rules, and security analytics Knowledge of cloud platforms (AWS, Azure, GCP), Kubernetes, containers, DevOps and CI/CD practices. Experience with OpenTelemetry and modern observability architectures is preferred. Understanding of data architecture, performance sizing, scalability, and high availability concepts. Hands-on experience with AI-powered search, vector databases, semantic search, GenAI applications, and MLOps. Design and implement Retrieval-Augmented Generation (RAG) pipelines for LLM-based applications. Integrate LLMs with Elasticsearch search pipelines and enterprise knowledge retrieval systems. Can define and measure RAG quality metrics and retrieval effectiveness. Design vector indexing and embedding strategies. Experience with Elastic Learned Sparse Encoder (ELSER) and Model Context Protocol (MCP). Build intelligent search assistants and enterprise search platforms. Develop and deploy ML solutions for Classification, Anomaly Detection, Recommendation Systems & Forecasting. Create evaluation frameworks to measure retrieval accuracy and search relevance. Must have skill to Build AI observability dashboards and monitoring frameworks. Define and manage SLI/SLO frameworks. Hands on experiences with rules & connectors. Experience in backend/frontend development using Java , Node.js, NoSQL query & painless scripting. Develop predictive alerting and anomaly detection mechanisms for proactive system monitoring, Presales Skills : Proven experience in enterprise presales, solution consulting, or customer-facing architecture roles. Ability to conduct technical discovery and map customer challenges to business outcomes. Strong presentation, communication, and stakeholder management skills. Ability to explain complex technical solutions to both technical and business audiences. Experience creating solution documents, HLD/LLD, proposals, and architecture diagrams. Strong analytical thinking and problem-solving capability. Ability to work independently in a fast-paced and evolving environment. Certifications : Required : anyone of the below Elastic Certified Analyst Elastic Certified Observability Engineer Elastic Certified SIEM Analyst Elastic Certified Engineer Education : Bachelor’s degree or higher in Computer Science, Information Technology, or related field.