Forward Deployed AI Engineer

Company: Trida Labs
Location: Bengaluru, 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 and analytics more effectively.

Our products include:

TridaPad — An AI-native data collaboration platform for querying, visualizing, collaborating on, and managing data across SQL, NoSQL, Big Data, and cloud data sources.
AgentSQL — An agentic AI analytics solution integrated with TridaPad that enables users to interact with enterprise data using natural language, generate SQL, analyze data, create visualizations and dashboards, and discover insights.
TridaPad MCP Server — A Model Context Protocol server that enables AI assistants and MCP-compatible clients to interact with TridaPad and its data and analytics capabilities.

About the Role

We are looking for a Forward Deployed AI Engineer to work directly with customers and engineering teams to deploy, configure, evaluate, and improve AI-powered data and analytics solutions.

You will primarily work with AgentSQL and TridaPad, adapting existing AI capabilities to customer-specific data, business terminology, metrics, analytical requirements, and workflows.

This role combines customer-facing engineering with AI applications, LLMs, SQL, enterprise data, agent workflows, production troubleshooting, and technical implementation.

What You'll Do

1. AgentSQL & AI Configuration

Deploy and configure AgentSQL for customer-specific data, schemas, business terminology, metrics, and workflows.
Adapt existing prompts, context, tools, and AI workflows to customer requirements.
Configure and validate natural-language-to-SQL, data retrieval, analysis, visualization, dashboard, and insight workflows.
Investigate incorrect or unreliable AI outputs and determine whether the issue is related to data, configuration, context, model behavior, or product functionality.
Work with AI Agent, AI, NLP/ML, and Data & AI teams when issues require changes to core product capabilities.
Use existing agent and tool-calling capabilities to support multi-step analytics workflows rather than building customer-specific forks.

2. Customer Evaluation & AI Quality

Build customer-specific golden question sets containing representative business questions, expected SQL, and verified analytical results.
Test AgentSQL against customer schemas, terminology, metrics, permissions, and real-world business questions.
Rerun relevant test cases after product upgrades, model changes, prompt changes, or configuration changes.
Validate that generated dashboards, visualizations, summaries, and insights accurately reflect the underlying analytical results.
Track regressions and recurring failure patterns and provide actionable feedback to engineering teams.
Maintain customer-specific prompts, context, and configuration as versioned implementation artifacts.

3. Enterprise Data & Integrations

Integrate AgentSQL and TridaPad with customer databases and data platforms such as PostgreSQL, MySQL, SQL Server, MongoDB, Snowflake, BigQuery, and Databricks.
Configure and troubleshoot database connections, permissions, schemas, and query access.
Understand customer data models, relationships, terminology, and business metrics.
Validate that customer data is correctly accessible and represented within the application.
Troubleshoot SQL, connectivity, permission, schema, and data-access issues across customer environments.

4. Security, MCP & AI Integrations

Support customer security questionnaires, technical reviews, and access requirements during pilots and deployments.
Work with customer teams on VPNs, IP allowlisting, private connectivity, restricted networks, or other access controls.
Support self-hosted deployments in customer cloud or on-premises environments where applicable.
Configure least-privilege database access and follow company and customer data-handling requirements.
Handle customer data, PII, credentials, logs, screenshots, and debugging information appropriately.
Configure and validate TridaPad MCP Server integrations with AI assistants, internal applications, and MCP-compatible clients.
Test authentication, permissions, tool access, and expected behavior across MCP integrations.

5. Customer Deployment & Troubleshooting

Work directly with customers to understand their data, business requirements, technical constraints, and analytics workflows.
Support product demonstrations, technical evaluations, pilots, and proofs of concept.
Translate customer requirements into practical configurations, integrations, AI workflows, and implementation plans.
Deploy and configure TridaPad and AgentSQL in customer environments.
Investigate production issues across AI, SQL, databases, APIs, integrations, and application workflows.
Use logs, traces, configuration details, and reproduction steps to isolate root causes.
Distinguish customer-specific requirements from broader product capabilities and escalate core changes to the appropriate engineering team.
Validate fixes and upgrades against customer-relevant test cases before production rollout.

6. Customer Delivery & Product Feedback

Support customer workshops, deployments, go-live activities, and post-deployment validation.
Manage multiple customer implementation activities while maintaining clear priorities and communication.
Gather customer feedback and identify recurring technical or product requirements.
Turn repeated implementation challenges into reusable configurations, tooling, documentation, or product requirements.
Work with engineering teams to ensure customer-specific work does not become an unsupported product fork.
Support relevant customer time zones and travel for customer meetings, workshops, deployments, or go-live activities when required.

7. Production AI Engineering

Support AI applications running in production using Docker and cloud or customer-managed infrastructure.
Monitor AI workflow behavior, latency, errors, resource usage, and LLM usage during customer deployments.
Troubleshoot issues across AI, application, database, and integration layers.
Work with Platform Engineering on deployment, observability, infrastructure, security, and environment-level issues.
Contribute to reliable and repeatable deployment and upgrade processes.
Use deterministic application logic, APIs, and tools where an LLM is not necessary for a customer workflow.

Role Ownership

Forward Deployed AI Engineer: Owns customer discovery, AI-focused implementation, configuration, integration, evaluation, validation, deployment, and customer-facing technical delivery.
AI Engineer: Owns reusable user-facing AI features and AI-powered application experiences.
AI Agent Engineer: Owns core agent orchestration, agent behavior, tool workflows, and 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 posture, and platform-level connectivity. Platform also owns the installer and upgrade path for supported self-hosted deployments.
Backend Engineer: Owns core backend services, APIs, business logic, and shared application services.

The Forward Deployed AI Engineer runs and validates existing deployment and upgrade tooling rather than owning the underlying platform or installer. Company-wide security policies and standard security responses are owned by the appropriate Platform/leadership functions; the FDE provides customer-specific technical information and implementation details.

What We're Looking For

3–5 years of experience in software engineering, data engineering, AI engineering, solutions engineering, or a similar technical role.
Strong proficiency in Python and SQL.
Experience working with enterprise databases, APIs, backend services, and data integrations.
Experience troubleshooting production applications and customer-specific technical issues.
Ability to understand customer requirements and translate them into practical technical solutions.
Experience working directly with customers, stakeholders, or cross-functional technical teams.
Strong debugging, analytical, and problem-solving skills.
Hands-on familiarity with LLMs, Generative AI, or AI-powered applications.
Strong communication skills and the ability to explain technical issues clearly to both customers and engineering teams.

Preferred Qualifications

Experience in Forward Deployed Engineering, Solutions Engineering, Sales Engineering, Technical Consulting, or customer-facing technical roles.
Experience with natural-language-to-SQL, text-to-SQL, AI analytics, or enterprise data applications.
Experience with AI agents, tool calling, or agentic workflows.
Experience with LangGraph, LangChain, LlamaIndex, or similar frameworks.
Experience with MCP and AI assistant integrations.
Experience with AI evaluation, regression testing, observability, or production AI troubleshooting.
Experience with 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 pilots, proofs of concept, technical demonstrations, or self-hosted customer deployments.
Experience optimizing LLM latency, token usage, model calls, or inference costs.

Key Skills

Why Join Trida Labs?

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

You will work across customer discovery, AI implementation, enterprise data integration, evaluation, production troubleshooting, and product improvement while helping turn real customer requirements into reliable, reusable AI 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.

Trida Network

Forward Deployed AI Engineer