Bengaluru, Karnataka (Hybrid/Onsite)

2 - 4 years of experience

About Lyzr:

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

1. 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.

2. 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).

3. 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.

1. 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.

2. Architecture Documentation: Create detailed technical documentation including solution architecture diagrams, data flow maps, integration specifications, security assessments, and implementation guides for customer engineering teams.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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.

10. 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.

11. Risk Assessment & Mitigation: Identify technical risks, dependencies, and blockers in customer deployments. Develop mitigation strategies and contingency plans to ensure successful implementations.

12. Post-Deployment Support: Monitor deployed solutions, analyze performance metrics, and recommend optimization strategies. Support customers through agent lifecycle management including retraining, versioning, and updates.

13. Bachelor's or Master's degree in Computer Science, Engineering, or related technical field.

14. 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.

15. Strong expertise in AI/ML technologies including LLMs, RAG (Retrieval-Augmented Generation), agent frameworks, vector databases, and generative AI application development.

1. Proven experience designing and deploying production AI/ML systems for enterprise customers.

2. Deep understanding of cloud platforms (AWS, Azure, GCP) including compute, storage, networking, security, and AI/ML services (SageMaker, Vertex AI, Azure AI).

3. Experience with enterprise integration patterns, APIs, microservices architectures, message queues, and ETL/data pipeline design.

4. Strong knowledge of security best practices, data privacy regulations (GDPR, HIPAA, SOC2), and compliance frameworks relevant to enterprise AI deployments.

5. Excellent communication skills with ability to engage effectively with both technical teams (engineers, data scientists) and business stakeholders (executives, product managers).

6. Experience working in customer-facing roles with demonstrated ability to manage complex stakeholder relationships.

7. Ability to translate business requirements into technical architectures and communicate technical concepts to non-technical audiences.

Preferred Qualifications

1. Experience with agentic AI frameworks such as Lyzr, LangChain, LangGraph, LlamaIndex, AutoGPT, or similar orchestration tools.

2. Hands-on experience deploying LLM-based applications in production environments with proper governance, monitoring, and observability.

3. Knowledge of BFSI domain, banking operations, insurance workflows, regulatory compliance, or other enterprise verticals.

4. Experience with Agile methodologies and participating in sprint-based development cycles.

5. Familiarity with MLOps practices, model lifecycle management, CI/CD pipelines for ML, and tools like MLflow, Kubeflow, or similar.

6. Understanding of prompt engineering, fine-tuning, RAG architectures, and LLM optimization techniques.

7. Experience with containerization (Docker, Kubernetes) and infrastructure-as-code (Terraform, CloudFormation).

8. Background in software development with proficiency in Python, JavaScript/TypeScript, or other modern languages.

9. Previous experience working with enterprise platforms such as Salesforce, SAP, ServiceNow, or core banking systems.

10. Certifications in cloud platforms (AWS Certified Solutions Architect, Azure Solutions Architect, GCP Professional Cloud Architect) or AI/ML specializations.

Key Skills

1. Solutions architecture and enterprise system design

2. AI/ML system architecture and deployment

3. LLM application development and agent orchestration

4. Cloud infrastructure (AWS, Azure, GCP)

5. Enterprise integration patterns and API design

6. Security architecture and compliance frameworks

7. Data engineering and pipeline design

8. Technical communication and presentation

9. Customer relationship management and consulting

10. Problem-solving under ambiguity

11. Documentation and technical writing

12. Stakeholder management across technical and business teams

Technical Competencies

1. AI/ML Stack: LLMs (GPT-4, Claude, Gemini, open-source models), Lyzr, LangChain, LangGraph, LlamaIndex, vector databases (Pinecone, Weaviate, Chroma), embedding models, RAG architectures

2. Cloud Platforms: AWS (EC2, Lambda, S3, SageMaker, Bedrock), Azure (App Service, Azure AI, Cognitive Services), GCP (Compute Engine, Vertex AI)

3. Data & Infrastructure: PostgreSQL, MongoDB, Redis, Kafka, data pipeline design, ETL workflows

4. Integration: REST APIs, GraphQL, webhooks, microservices, message queues, event-driven architectures

5. Security: Encryption (at rest, in transit), SSO/SAML, RBAC, OAuth, API security, compliance (SOC2, GDPR, HIPAA)

6. DevOps/MLOps: Docker, Kubernetes, CI/CD (GitHub Actions, Jenkins), infrastructure-as-code, monitoring (Prometheus, Grafana, Datadog)

7. Programming: Python (primary), JavaScript/TypeScript, SQL, shell scripting