AI Engineer
Trida AI
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Benefits offered by this job
Job summary
Trida Labs is seeking an AI Engineer to build and improve user-facing AI features for TridaPad and related analytics products in Chennai, India. You will own AI-assisted query experiences, SQL explanations, and data-driven summaries while ensuring reliable, transparent AI outcomes.
You will work with LLMs, retrieval, evaluation, and product APIs to turn enterprise data into useful insights, dashboards, and explanations for users. 3-5 years of related experience is preferred.
Qualifications
- 3-5 years of AI/software engineering experience.
- Hands-on experience with LLM-powered applications.
- Experience with enterprise data and analytics.
Responsibilities
- Build AI-powered features to query, understand, and work with enterprise data.
- Develop AI-assisted SQL explanations, query suggestions, and SQL error assistance.
- Build chart-type recommendations and AI-assisted dashboard layouts.
Skills
Tools
Job description
Company: Trida Labs
Location: Chennai ,India
Employment Type: Full-time
Experience: 3-5 Years
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 managing data across SQL, NoSQL, Big Data, and cloud data sources.
AgentSQL — An agentic AI analytics solution that enables users to interact with enterprise data using natural language, generate SQL, perform analysis, create visualizations and dashboards, and derive insights.
TridaPad MCP Server — A Model Context Protocol server that enables AI assistants and MCP-compatible clients to interact with TridaPad and its data capabilities.
We are looking for an AI Engineer to build and improve the user-facing AI features of TridaPad and its AI-powered analytics products.
You will own features such as AI-assisted query experiences, SQL explanations and error assistance, chart and dashboard recommendations, and data-driven summaries and insights. You will work with LLMs, structured outputs, retrieval, evaluation, and product APIs to turn enterprise data into useful and trustworthy user experiences.
The Data & AI team provides the data connectivity, schema, metadata, and semantic foundations. The AI Agent team owns agent orchestration and agent behavior. The NLP/ML team focuses on model and generation quality. This role focuses on applying those capabilities to reliable, user-facing AI product features.
Build AI-powered features that help users query, understand, and work with enterprise data.
Develop AI-assisted SQL explanations, query suggestions, and SQL error assistance.
Build chart-type recommendations and AI-assisted dashboard layouts based on query results.
Generate plain-language summaries and explanations of analytical results.
Develop AI experiences that help users understand AgentSQL findings and analytical outputs.
Design features that make AI-generated results useful, transparent, and easy to review.
Design and develop production-ready applications using LLMs and Generative AI.
Integrate LLMs and model providers based on application requirements.
Develop prompts, context strategies, structured outputs, and AI workflows.
Use typed and validated outputs where AI responses are consumed by application features.
Improve the accuracy, consistency, reliability, and performance of AI-powered features.
Prefer deterministic application logic when an LLM does not provide meaningful value.
Build user-facing workflows around natural-language queries, SQL, analysis, and visualization.
Present generated SQL clearly and allow users to review or edit it where appropriate.
Help users understand how an analytical result was produced.
Connect generated explanations and insights to the underlying query results.
Handle ambiguous requests and unsupported questions appropriately.
Provide useful fallback or clarification experiences when an AI workflow cannot produce a reliable answer.
Build workflows that generate explanations, summaries, and insights from actual analytical results.
Ensure figures, percentages, totals, trends, and other numerical claims in generated text are grounded in query results.
Validate generated summaries against the underlying data before presenting them to users.
Prevent AI-generated explanations from introducing unsupported numbers or conclusions.
Distinguish between observed results, calculated metrics, and model-generated explanations.
Investigate cases where generated insights do not accurately represent the underlying data.
Use the schema, metadata, and semantic capabilities provided by the Data & AI layer to build AI features.
Retrieve relevant context required for user-facing AI workflows.
Work with RAG, embeddings, and vector databases where they provide clear product value.
Improve context selection and assembly for specific AI features.
Evaluate whether retrieved context is relevant and sufficient for the requested task.
Collaborate with Data & AI engineers when additional metadata or context capabilities are required.
Consume agent and tool capabilities provided by the AI Agent layer to build user-facing product features.
Integrate AI features with APIs, application services, data tools, and existing product workflows.
Use tool and function calling where required by the feature.
Integrate with the TridaPad MCP Server where appropriate.
Work with AI Agent engineers when a feature requires new agent tools or orchestration capabilities.
Avoid duplicating underlying agent infrastructure or orchestration that is owned by the AI Agent team.
Build evaluation datasets and test cases for user-facing AI features.
Evaluate response correctness, relevance, consistency, and usability.
Test generated SQL explanations, summaries, insights, visualizations and dashboard recommendations.
Verify that numerical claims and generated explanations are grounded in actual query results.
Test AI behavior against ambiguous, incomplete, and unsupported user requests.
Identify recurring failure patterns and investigate their root causes.
Compare prompts, models, context strategies, and workflow changes based on evaluation results.
Establish repeatable evaluation and regression testing for production AI features.
Design AI experiences that make generated outputs understandable and reviewable.
Clearly present generated SQL and analytical results to users.
Provide appropriate traceability between insights and their underlying results.
Handle uncertainty and low-confidence situations without presenting unsupported conclusions as facts.
Design useful clarification, retry, and fallback experiences.
Work with product and frontend engineers to integrate AI capabilities into the user experience.
Optimize AI features for quality, latency, reliability, and cost.
Reduce unnecessary model calls and token usage where appropriate.
Improve prompt and context efficiency while maintaining output quality.
Evaluate model and workflow trade‑offs based on product requirements.
Investigate performance issues across AI application workflows.
Measure and improve the practical performance of production AI features.
Deploy and support AI-powered features across development, staging, and production environments.
Troubleshoot issues across AI, application, API, database, and service layers.
Analyze logs, errors, model responses, and workflow behavior to identify root causes.
Work with platform engineers on monitoring, logging, tracing, and AI observability.
Improve the reliability, scalability, performance, and maintainability of production AI features.
Participate in production issue investigation and resolution.
Work closely with Product, AI Agent, Data & AI, NLP/ML, Backend, Frontend, and Platform engineers.
Translate product requirements into practical AI solutions.
Contribute to technical design and architecture discussions.
Define clear interfaces between AI features and underlying data, agent, and platform capabilities.
Build reusable AI components and application services.
Document AI workflows, evaluation approaches, technical decisions, and implementations.
- AI Engineer: Owns user-facing AI features, AI-assisted analytics experiences, insight generation, AI UX, and application-level AI workflows.
- Data & AI Engineer: Owns data connectivity, schema, metadata, semantic representations, and enterprise data context.
- AI Agent Engineer: Owns agent orchestration, tools, agent workflows, and agent behavior.
- ML/NLP Engineer: Owns model development, NLP capabilities, and generation quality.
- AI Platform Engineer: Owns model infrastructure, deployment, observability, security, and platform reliability.
- 3-5 years of experience in AI engineering, software engineering, ML engineering, or a related field.
- Strong proficiency in Python.
- Hands-on experience building LLM-powered or Generative AI applications.
- Experience integrating AI models with APIs, application services, tools, or data systems.
- Strong understanding of prompt engineering, context engineering, and structured AI outputs.
- Experience building production software and user-facing application features.
- Understanding of RAG, embeddings, vector databases, or retrieval systems.
- Experience evaluating and improving AI application quality.
- Strong debugging, problem-solving, and analytical skills.
- Strong communication and collaboration skills.
- Experience with LangGraph, LangChain, LlamaIndex, or similar frameworks.
- Experience with AI agents and tool/function calling.
- Experience with MCP and AI assistant integrations.
- Experience building AI-powered data or analytics applications.
- Experience with natural-language-to-SQL or AI-assisted query experiences.
- Experience with AI evaluation, regression testing, or quality measurement.
- Experience with structured outputs and schema validation for LLM applications.
- Experience optimizing model usage, latency, token consumption, and inference costs.
- Experience with enterprise databases such as PostgreSQL, MySQL, SQL Server, Snowflake, BigQuery, Databricks, or MongoDB.
- Experience with Docker and cloud and deployment platforms such as AWS, Azure, GCP, Render, or Vercel.
- Familiarity with CI/CD, testing, monitoring, and production engineering practices.
Python | LLMs | Generative AI | AI Applications | Prompt Engineering | Context Engineering | Structured Outputs | RAG | AI Evaluation | Natural-Language-to-SQL | AI Analytics | Data Visualization | APIs | Tool Calling | MCP | LangGraph | LangChain | Docker | Cloud
- Opportunity to build production AI features used with real enterprise data.
- Hands-on experience across LLM applications, AI analytics, data visualization, and intelligent product experiences.
- Opportunity to solve practical problems involving AI reliability, grounding, evaluation, and user trust.
- Exposure to modern AI technologies and production engineering practices.
- Collaborative environment focused on building practical enterprise AI products.
At Trida Labs, you will build AI features that help users work with complex enterprise data more effectively.
You will work at the intersection of AI engineering, analytics, and product development, turning LLM and AI capabilities into reliable features that users can understand, review, and trust.
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.
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