Bengaluru, Karnataka (Hybrid/Onsite)
About Lyzr
2 - 4 years of experience
Lyzr is an enterprise AI agent platform helping companies deploy production-grade AI faster than anywhere else. We're growing fast across product, engineering, and go-to-market and we're looking for a People leader to grow and develop the function.
This is a rare chance to own the entire people's agenda at a company that's past the experimental stage but still early enough that your decisions will shape culture for years.
Role Overview
We are seeking an experienced Solutions Architect – Applied AI to join our Applied AI team in Bengaluru. This role sits at the intersection of enterprise architecture, AI/ML systems, and customer success. You will serve as the technical bridge between Lyzr's platform capabilities and customer business objectives, designing scalable AI agent solutions that solve complex enterprise problems.
As a Solutions Architect, you'll work directly with enterprise customers, account executives, engineering teams, and product managers to translate business requirements into production-grade AI architectures. You'll be responsible for the end-to-end technical success of customer deployments – from discovery and architecture design to implementation oversight and optimization.
This role requires deep technical expertise in AI/ML systems, enterprise integration patterns, and the ability to communicate complex technical concepts to both C-suite executives and engineering teams.
Key Responsibilities
- Customer-Facing Technical Leadership: Partner with account executives and customer stakeholders to understand business requirements, map workflows, and design AI agent solutions that align with enterprise objectives and constraints.
- AI Solution Architecture Design: Develop comprehensive architectural blueprints for agentic AI deployments, including data pipelines, agent orchestration patterns, LLM selection, integration strategies, security frameworks, and deployment models (cloud, on-premise, hybrid).
- Enterprise Integration: Design and oversee integrations between Lyzr's agent platform and customer systems such as Salesforce, SAP, ServiceNow, core banking platforms, ERP systems, CRM tools, and custom enterprise applications.
- Technical Advisor Throughout Customer Journey: Serve as the primary technical advisor to enterprise customers from discovery through evaluation, proof-of-concept (POC), pilot deployment, and production rollout. Coordinate across internal teams to drive customer success.
- Architecture Documentation: Create detailed technical documentation including solution architecture diagrams, data flow maps, integration specifications, security assessments, and implementation guides for customer engineering teams.
- Technology Evaluation & Recommendation: Evaluate AI frameworks (LangChain, LlamaIndex, custom agent frameworks), LLM providers (OpenAI, Anthropic, open-source models), vector databases, orchestration tools, and deployment platforms based on customer requirements.
- Security & Compliance Design: Embed security, privacy, and compliance measures into solution architectures. Ensure designs meet requirements for SOC2, GDPR, HIPAA, PCI DSS, and industry-specific regulations. Design for data encryption, anonymization, access controls, and audit trails.
- Implementation Oversight: Guide customer engineering teams and Lyzr implementation teams during deployment phases. Review code, configurations, system integrations, and performance optimizations to ensure alignment with architectural vision.
- Performance Optimization: Design solutions for scalability, reliability, and cost-efficiency. Implement monitoring, observability, and performance tuning strategies to ensure AI agents operate effectively under production workloads.
- Technical Enablement & Workshops: Conduct technical workshops, architecture review sessions, and enablement programs for customer teams. Create technical content including reference architectures, best practices guides, and deployment playbooks.
- Thought Leadership: Develop and deliver compelling technical presentations to C-suite, VP-level stakeholders, and technical teams. Translate complex AI architectures into business value propositions and ROI narratives.
- Cross-Functional Collaboration: Work closely with Lyzr's product, engineering, DevOps, and customer success teams to ensure customer feedback informs product development and platform improvements.
- Agent Use Case Development: Design solutions for enterprise agent use cases including AI SDR (sales development), customer support automation, supplier onboarding, contract analysis, compliance monitoring, knowledge search, RFP processing, and back-office automation.
- Risk Assessment & Mitigation: Identify technical risks, dependencies, and blockers in customer deployments. Develop mitigation strategies and contingency plans to ensure successful implementations.
- Post-Deployment Support: Monitor deployed solutions, analyze performance metrics, and recommend optimization strategies. Support customers through agent lifecycle management including retraining, versioning, and updates.
- Bachelor's or Master's degree in Computer Science, Engineering, or related technical field.
- 2-4 years of experience in solutions architecture, enterprise architecture, or technical consulting roles with focus on AI/ML systems, cloud infrastructure, or enterprise software.
- Strong expertise in AI/ML technologies including LLMs, RAG (Retrieval-Augmented Generation), agent frameworks, vector databases, and generative AI application development.
- Proven experience designing and deploying production AI/ML systems for enterprise customers.
- Deep understanding of cloud platforms (AWS, Azure, GCP) including compute, storage, networking, security, and AI/ML services (SageMaker, Vertex AI, Azure AI).
- Experience with enterprise integration patterns, APIs, microservices architectures, message queues, and ETL/data pipeline design.
- Strong knowledge of security best practices, data privacy regulations (GDPR, HIPAA, SOC2), and compliance frameworks relevant to enterprise AI deployments.
- Excellent communication skills with ability to engage effectively with both technical teams (engineers, data scientists) and business stakeholders (executives, product managers).
- Experience working in customer-facing roles with demonstrated ability to manage complex stakeholder relationships.
- Ability to translate business requirements into technical architectures and communicate technical concepts to non-technical audiences.
Preferred Qualifications
- Experience with agentic AI frameworks such as Lyzr, LangChain, LangGraph, LlamaIndex, AutoGPT, or similar orchestration tools.
- Hands-on experience deploying LLM-based applications in production environments with proper governance, monitoring, and observability.
- Knowledge of BFSI domain, banking operations, insurance workflows, regulatory compliance, or other enterprise verticals.
- Experience with Agile methodologies and participating in sprint-based development cycles.
- Familiarity with MLOps practices, model lifecycle management, CI/CD pipelines for ML, and tools like MLflow, Kubeflow, or similar.
- Understanding of prompt engineering, fine-tuning, RAG architectures, and LLM optimization techniques.
- Experience with containerization (Docker, Kubernetes) and infrastructure-as-code (Terraform, CloudFormation).
- Background in software development with proficiency in Python, JavaScript/TypeScript, or other modern languages.
- Previous experience working with enterprise platforms such as Salesforce, SAP, ServiceNow, or core banking systems.
- Certifications in cloud platforms (AWS Certified Solutions Architect, Azure Solutions Architect, GCP Professional Cloud Architect) or AI/ML specializations.
Key Skills
- Solutions architecture and enterprise system design
- AI/ML system architecture and deployment
- LLM application development and agent orchestration
- Cloud infrastructure (AWS, Azure, GCP)
- Enterprise integration patterns and API design
- Security architecture and compliance frameworks
- Data engineering and pipeline design
- Technical communication and presentation
- Customer relationship management and consulting
- Problem-solving under ambiguity
- Documentation and technical writing
- Stakeholder management across technical and business teams
Technical Competencies
- AI/ML Stack: LLMs (GPT-4, Claude, Gemini, open-source models), Lyzr, LangChain, LangGraph, LlamaIndex, vector databases (Pinecone, Weaviate, Chroma), embedding models, RAG architectures
- Cloud Platforms: AWS (EC2, Lambda, S3, SageMaker, Bedrock), Azure (App Service, Azure AI, Cognitive Services), GCP (Compute Engine, Vertex AI)
- Data & Infrastructure: PostgreSQL, MongoDB, Redis, Kafka, data pipeline design, ETL workflows
- Integration: REST APIs, GraphQL, webhooks, microservices, message queues, event-driven architectures
- Security: Encryption (at rest, in transit), SSO/SAML, RBAC, OAuth, API security, compliance (SOC2, GDPR, HIPAA)
- DevOps/MLOps: Docker, Kubernetes, CI/CD (GitHub Actions, Jenkins), infrastructure-as-code, monitoring (Prometheus, Grafana, Datadog)
- Programming: Python (primary), JavaScript/TypeScript, SQL, shell scripting
