Forward Deployed Engineer

Company: Trida Labs
Location: Hyderabad, India
Employment Type: Full-time
Experience: 3–5 Years

About Trida Labs

Trida Labs builds AI-native products that help organizations work with enterprise data, analytics, and intelligent applications.

Our products include TridaPad, an AI-native data collaboration platform for querying, analyzing, visualizing, and working with data across enterprise data sources; AgentSQL, an agentic AI analytics solution for natural-language interaction with enterprise data; and TridaPad MCP Server, which enables AI assistants and MCP-compatible clients to interact with TridaPad capabilities.

About the Role

We are looking for a Forward Deployed Engineer to work directly with customers and turn business and technical requirements into working solutions using Trida Labs products.

You will work across customer discovery, data integration, configuration, testing, deployment, and production troubleshooting. You will understand customer workflows, data environments, security requirements, and business needs, then configure and integrate our products to solve real customer problems.

The role also involves supporting customer pilots and pre-sales engagements, validating solutions against customer-specific use cases, and working closely with engineering teams when product-level changes are required.

This is a customer-facing engineering role. You will configure and extend existing capabilities rather than build customer-specific forks, and you will escalate core product requirements to the appropriate engineering team.

What You'll Do1. Customer Discovery & Solution Design

• Understand customer business workflows, technical requirements, data environments, and operational constraints.

• Translate customer requirements into practical Trida Labs solutions.

• Identify required data sources, integrations, configurations, permissions, and deployment requirements.

• Assess technical feasibility and communicate implementation trade-offs clearly.

• Define implementation approaches that can be reused across customers where possible.

2. Pre-Sales, Pilots & Proofs of Concept

• Support technical customer discussions, product demonstrations, pilots, and proofs of concept.

• Understand customer use cases and demonstrate how TridaPad and AgentSQL can address them.

• Configure representative customer environments for evaluation.

• Validate technical requirements and identify integration or security constraints before deployment.

• Communicate technical feasibility, limitations, and implementation requirements to product and sales stakeholders.

3. Product Implementation & Data Integration

• Deploy and configure TridaPad and AgentSQL for customer use cases.

• Configure data sources, database connections, permissions, schemas, settings, and analytics workflows.

• Work with customer databases and data platforms such as PostgreSQL, MySQL, SQL Server, MongoDB, Snowflake, BigQuery, and Databricks.

• Understand customer data models, business metrics, relationships, and query requirements.

• Validate that connected data is accessible and correctly represented in the application.

• Troubleshoot connection, permission, schema, query, and integration issues.

4. AI & Analytics Implementation

• Configure existing AI and analytics capabilities for customer requirements.

• Adapt prompts, context, tools, and application workflows where configuration is appropriate.

• Validate natural-language analytics, SQL generation, analytical results, visualizations, dashboards, and generated summaries.

• Diagnose AI-output issues and determine whether they are related to data, configuration, context, model behavior, or product functionality.

• Work with the relevant engineering teams when issues require changes to core AI capabilities.

5. Security, Access & MCP Integrations

• Support customer security questionnaires, technical reviews, and access requirements.

• Coordinate requirements for VPNs, IP allowlisting, private connectivity, restricted networks, and other customer-specific access controls.

• Support self-hosted deployments in customer cloud or on-premises environments where applicable.

• Apply least-privilege principles when configuring customer database access.

• Follow customer and company requirements for handling production data, PII, credentials, logs, screenshots, and debugging information.

• Configure and test MCP-based integrations between TridaPad, AI assistants, internal applications, and customer workflows.

• Validate authentication, permissions, tool access, and expected behavior across integrations.

6. Customer Evaluation & Validation

• Build customer-specific golden question sets containing representative business questions, expected SQL, and verified analytical results.

• Use these test cases to validate customer configurations and AI workflows.

• Rerun relevant test cases after product upgrades, model changes, prompt changes, or configuration changes.

• Validate SQL, analytical results, visualizations, dashboards, and generated summaries against expected outcomes.

• Track regressions and communicate failures with sufficient technical context for engineering teams.

• Maintain customer-specific prompts, context, and configuration as versioned implementation artifacts.

7. Deployment & Production Troubleshooting

• Create implementation plans and technical validation checklists for customer deployments.

• Support deployments across development, staging, and production environments.

• Troubleshoot application, API, database, AI, integration, and deployment issues.

• Use logs, errors, traces, configuration details, and reproduction steps to isolate problems.

• Validate engineering fixes in customer-relevant scenarios before production deployment where appropriate.

• Support production releases, rollouts, upgrades, and post-deployment validation.

8. Customer Feedback & Engineering Collaboration

• Identify recurring customer problems and implementation challenges.

• Distinguish customer-specific requirements from broader product capabilities.

• Provide structured product feedback with technical context and customer impact.

• Identify opportunities to turn repeated implementation work into reusable product capabilities, tooling, or documentation.

• Work closely with AI, AI Agent, Data & AI, Backend, Platform, NLP/ML, and Product teams.

• Provide clear requirements, reproduction steps, logs, expected behavior, and customer context when escalating issues.

• Help reduce customer-specific workarounds and avoid unnecessary product forks.

Role Ownership

• Forward Deployed Engineer: Owns customer discovery, technical solution design, implementation, integration, configuration, validation, deployment, troubleshooting, and customer-facing technical delivery.

• AI Engineer: Owns reusable user-facing AI features and AI-powered application experiences.

• AI Agent Engineer: Owns agent orchestration, agent behavior, tool workflows, and core agent capabilities.

• Data & AI Engineer: Owns data connectivity, schema and metadata systems, semantic representations, and cross-database data correctness.

• NLP/ML Engineer: Owns model-level NLP and generation quality, including SQL-generation model capabilities.

• Platform Engineer: Owns infrastructure, model access, deployment systems, observability, security, and platform-level connectivity.

• Backend Engineer: Owns core backend services, APIs, business logic, and shared application services.

The Forward Deployed Engineer configures and extends existing capabilities, provides customer context to specialist teams, and escalates core product changes rather than maintaining customer-specific forks.

Customer Engagement Expectations

• Work directly with customers across different technical and business environments.

• Support customer deployments and meetings across relevant customer time zones when required.

• Be willing to travel for customer meetings, deployments, workshops, or go-live activities when required.

• Manage multiple customer implementation activities while maintaining clear priorities and communication.

What We're Looking For

• 3–5 years of experience in software engineering, solutions engineering, data engineering, AI engineering, technical consulting, or a similar technical role.

• Strong Python and SQL skills.

• Experience working with APIs, databases, backend services, and enterprise applications.

• Experience integrating software with customer systems, databases, and data environments.

• Strong understanding of SQL, databases, data workflows, and technical troubleshooting.

• Experience working directly with customers, stakeholders, or cross-functional technical teams.

• Familiarity with LLMs, Generative AI, and AI-powered applications.

• Ability to understand customer requirements and translate them into practical technical solutions.

• Strong debugging and problem-solving skills across application, data, integration, and AI layers.

• Ability to troubleshoot issues using logs, errors, configuration details, and reproduction steps.

• Strong communication skills and the ability to explain technical issues clearly to both customers and engineering teams.

• Comfortable working with customer environments and supporting production deployments.

• Ability to manage multiple customer requirements while maintaining clear priorities and communication.

Preferred Qualifications

• Experience in Forward Deployed Engineering, Solutions Engineering, Sales Engineering, Technical Consulting, or Technical Implementation.

• Experience supporting product demonstrations, pilots, or proofs of concept.

• Experience with AI analytics, natural-language-to-SQL, or enterprise data applications.

• Experience with AI agents, tool calling, or agent workflows.

• Familiarity with LangGraph, LangChain, or LlamaIndex.

• Experience with MCP and AI assistant integrations.

• Experience with enterprise databases such as PostgreSQL, MySQL, SQL Server, MongoDB, Snowflake, BigQuery, or Databricks.

• Experience with Docker and cloud or deployment platforms such as AWS, Azure, GCP, Render, or Vercel.

• Experience supporting production deployments and customer environments.

• Familiarity with AI evaluation, regression testing, observability, or production AI troubleshooting.

Key Skills

Python | SQL | APIs | Databases | Enterprise Data | System Integration | Generative AI | LLMs | AI Applications | Natural-Language-to-SQL | Data Analytics | AI Agents | MCP | Docker | Cloud | Customer Deployment | Production Troubleshooting | Technical Implementation | Technical Communication

Why Join Trida Labs?

At Trida Labs, you will work directly on real AI systems solving enterprise data and analytics problems.

You will work closely with customers and engineering teams across solution design, AI implementation, enterprise data integration, evaluation, production troubleshooting, and product improvement. You will help turn real customer requirements into reliable, reusable product capabilities.

Equal Opportunity

Trida Labs is an equal opportunity employer. We are committed to creating an inclusive workplace where diverse perspectives are valued and everyone has the opportunity to contribute and grow.