Website:
riyalabs.ai
Job details:
About RiyaLabs
RiyaLabs builds enterprise AI software that helps organizations deploy AI coworkers and AI teams for real business workflows.
Our core platform, RiyaLabs Studio, enables users to collaborate with AI coworkers and orchestrated AI teams across finance, sales, procurement, human resources, business intelligence, operations, customer success, analytics, and other enterprise functions. Studio connects AI workflows with enterprise knowledge, data, and business systems to help teams research, analyze, create, coordinate, and execute work more efficiently.
RiyaLabs is also developing the RiyaLabs AI Secure Gateway, a secure and scalable control layer for enterprise AI. It governs access to models, data, MCP tools, and enterprise systems through policy controls, guardrails, approvals, token and usage monitoring, observability, and auditable execution.
We are looking for a Senior AI Product Engineer to help design, build, test, deploy, and improve these capabilities end to end.
Role Overview
As a Senior AI Product Engineer, you will build and enhance RiyaLabs Studio and related AI platform services.You will work across frontend applications, backend services, AI coworkers, multi-agent workflows, LLM integrations, RAG and knowledge systems, MCP tools, enterprise connectors, cloud deployment, testing, monitoring, and product reliability.
This is a hands-on role for an engineer who can turn business requirements into secure, production-ready software and own features from design through deployment and ongoing improvement.
Key ResponsibilitiesProduct and Full-Stack Development
- Design, build, test, deploy, and maintain full-stack capabilities for RiyaLabs Studio.
- Build user experiences for interacting with AI coworkers, AI teams, tasks, workflow results, approvals, activity history, and source-based outputs.
- Develop backend APIs, services, data models, background jobs, workflow-execution services, and integration layers.
- Build features for users, tenant workspaces, role-based access, conversations, task management, workflow status, audit logs, and administration.
- Translate business requirements into technical designs, implementation plans, estimates, and quality releases.
- Improve features using user feedback, telemetry, quality metrics, and product priorities.
- Write clean, maintainable, documented, and well-tested code.
AI Coworkers and Agentic Workflows
- Build AI coworkers and AI-team workflows for business use cases across finance, sales, procurement, HR, BI, operations, customer success, and analytics.
- Implement multi-agent orchestration, task delegation, workflow state, structured outputs, tool calling, retries, fallback behavior, error handling, and progress tracking.
- Develop reusable workflow components, prompts, skills, tool definitions, agent configurations, and task-planning patterns.
- Integrate LLM providers and support model configuration, token usage, cost controls, reliability, and provider fallback patterns.
- Build human-in-the-loop workflows for review, approval, clarification, and escalation.
- Create evaluations for response quality, grounding, citations, tool-use accuracy, completion rate, latency, reliability, and cost.
RAG, Data, and Knowledge Systems
- Build and improve RAG capabilities for enterprise knowledge workflows.
- Develop ingestion pipelines for PDFs, Word files, presentations, spreadsheets, web content, databases, and other enterprise data sources.
- Implement extraction, chunking, metadata enrichment, embeddings, vector indexing, semantic and hybrid search, reranking, citations, and grounded responses.
- Maintain tenant isolation, source-level access controls, document lineage, and secure handling of retrieved data.
MCP and Enterprise Integrations
- Build and maintain secure integrations with enterprise applications, APIs, databases, document repositories, and SaaS platforms.
- Develop MCP servers, MCP clients, tool adapters, API connectors, and secure tool-calling workflows.
- Work with systems such as Microsoft 365, SharePoint, Teams, SQL databases, Snowflake, ServiceNow, Salesforce, Jira, Confluence, Tableau, Power BI, and internal APIs.
- Implement authentication, authorization, schema validation, retries, idempotency, error handling, rate limiting, and monitoring.
- Develop reusable connector patterns and a tool registry for multiple workflows and customer environments.
- Ensure AI coworkers can access only approved data, tools, and actions based on user, workspace, role, and workflow permissions.
AI Security, Guardrails, and Reliability
- Contribute to the RiyaLabs AI Secure Gateway and platform controls for secure AI execution.
- Implement model routing, tool allow-lists, policy checks, token controls, approval workflows, and audit logging.
- Implement input/output validation, structured-output enforcement, safe rendering, data protection, and secure tool interactions.
- Support prompt-injection controls, PII and secret detection, data redaction, unsafe tool-use prevention, and policy-based restrictions.
- Build and maintain RBAC, tenant isolation, secure configuration, secrets management, API security, and least-privilege access.
- Support approval checkpoints for high-impact actions, including external communications, record updates, data exports, or workflow-triggered actions.
- Improve reliability through testing, logging, tracing, monitoring, alerting, troubleshooting, and performance optimization.
Cloud and Delivery
- Build cloud-ready components and support RiyaLabs-managed and customer-managed deployments.
- Contribute to CI/CD, environment configuration, release automation, infrastructure-as-code, and deployment documentation.
- Use Docker, Git, automated testing, logging, monitoring, tracing, and alerting.
- Support an Azure-first environment, with AWS or GCP exposure where needed.
- Work closely with product, engineering, implementation, and business stakeholders.
- Convert customer-specific needs into reusable product capabilities where possible.
Required Qualifications
- Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field; equivalent practical experience will be considered.
- 3–8 years of professional experience building and shipping web applications, backend systems, SaaS products, or enterprise software.
- Strong programming skills in Python and/or TypeScript/JavaScript.
- Experience with React, Next.js, TypeScript, or similar frontend technologies.
- Experience with FastAPI, Django, Flask, Node.js, NestJS, Express, or similar backend technologies.
- Hands-on experience building or integrating LLM-powered applications using OpenAI, Azure OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, open-source models, or equivalent platforms.
- Experience with prompt/context engineering, structured output, tool/function calling, workflow state, and error handling.
- Experience with RAG, embeddings, vector databases, semantic or hybrid search, document processing, and retrieval evaluation.
- Experience with REST APIs, webhooks, SQL or NoSQL databases, asynchronous processing, queues, caching, and background jobs.
- Experience integrating SaaS products, enterprise APIs, databases, or document repositories.
- Experience with Docker, Git, CI/CD, cloud deployment, environment configuration, logging, monitoring, and production debugging.
- Strong ownership, communication, problem-solving, and ability to work independently in a remote or distributed environment.
Preferred Qualifications
- Experience with LangGraph, LangChain, Semantic Kernel, AutoGen, CrewAI, OpenAI Agents SDK, or custom agent frameworks.
- Experience building or integrating MCP servers, MCP clients, AI tools, and enterprise tool-calling workflows.
- Experience with multi-agent systems, long-running workflows, workflow engines, event-driven architecture, queues, or durable execution patterns.
- Experience with Azure OpenAI, Azure Container Apps, AKS, Azure Functions, Azure Key Vault, Azure AI Search, Microsoft Entra ID, Application Insights, or Azure Monitor.
- Experience with AWS or GCP.
- Experience with Azure AI Search, pgvector, Pinecone, Weaviate, Qdrant, Elasticsearch, or OpenSearch.
- Experience integrating Microsoft 365, SharePoint, Teams, Snowflake, ServiceNow, Salesforce, Jira, Confluence, Tableau, Power BI, SAP, or other business systems.
- Experience with OAuth 2.0, OpenID Connect, JWT, SSO, RBAC, service identities, secrets management, and enterprise API security.
- Experience with Kubernetes, Terraform, Helm, infrastructure-as-code, and containerized deployments.
- Experience with OpenTelemetry, distributed tracing, LLM observability, token/cost monitoring, model evaluation, or AI performance monitoring.
- Experience implementing AI guardrails, PII redaction, prompt-injection controls, output validation, data-classification policies, or human-approval workflows.
Skills and Attributes
We are looking for someone who:
- Delivers working, maintainable solutions—not only prototypes or research.
- Can work across frontend, backend, AI workflows, data, integrations, and deployment.
- Breaks unclear business problems into clear technical tasks and staged delivery plans.
- Prioritizes reliability, security, testing, documentation, and maintainability.
- Applies sound engineering judgment while keeping up with evolving AI technologies.
- Understands that enterprise AI systems must be useful, safe, observable, and controllable.
- Communicates clearly with both technical and nontechnical stakeholders.
- Brings a practical, collaborative, and problem-solving mindset.
Why Join RiyaLabs
- Build practical enterprise AI products that solve meaningful business problems.
- Work across LLMs, RAG, multi-agent systems, MCP, enterprise integrations, cloud, and AI governance.
- Help create AI coworkers and AI teams that support real business operations.
- Take ownership of product capabilities and see them used in real workflows.
- Develop broad expertise across AI, full-stack engineering, cloud, data, security, and observability.
Click on Apply to know more.
