Data & AI Engineer

About the Job

Project Role: Data & AI Engineer
Company: Farmioc
Location: Chennai, India
Work Model: On-site
Employment Type: Full-time
Experience: 3–5 Years

About Farmioc

Farmioc is an agricultural data intelligence and commodity trading company focused on helping stakeholders across the agriculture value chain make better data-driven decisions.

Its platform brings together agricultural data, market intelligence, commodity trading, analytics, and decision-support solutions across products such as DataStore, PriceHub, MarketHub, AgriTools, and AGVISOR.

About the Role

We are looking for a Data & AI Engineer to build and maintain the data foundations that support Farmioc's agricultural intelligence and AI-enabled applications.

You will work across data integration, pipelines, SQL, databases, data quality, data modeling, retrieval, and AI-ready data workflows. You will help ensure that agricultural and commodity data is reliable, well-structured, discoverable, and usable across Farmioc's products.

The primary focus of this role is data engineering, with additional responsibility for preparing and retrieving data for AI and analytics applications.

What You'll DoData Engineering & Pipelines

Build and maintain data pipelines for agricultural, commodity, market, weather, supply, demand, yield, and related datasets.
Integrate data from internal systems, external providers, APIs, databases, and other sources.
Develop reliable workflows for data ingestion, transformation, validation, and processing.
Process structured and semi-structured data for use across Farmioc products.
Automate recurring data workflows and improve pipeline reliability.
Monitor pipeline execution and troubleshoot failed or incomplete data workflows.

DataStore & Data Integration

Work with Farmioc DataStore to organize, process, and integrate agricultural datasets from multiple sources.
Build workflows that allow datasets to be combined, compared, and analyzed consistently.
Work with metadata, data dictionaries, common terminology, schemas, and relationships between datasets.
Develop reusable data components and integrations for downstream applications.
Support data exports, automated updates, dashboards, and analytical workflows.

SQL, Databases & Data Modeling

Write and optimize SQL queries for agricultural and commodity datasets.
Design and maintain data models that support analytics, reporting, and AI applications.
Work with relational and NoSQL databases based on application and workload requirements.
Understand schemas, relationships, indexes, constraints, and query performance.
Troubleshoot database connectivity, data-access, and query-performance issues.
Improve data access patterns for frequently used datasets and analytical workflows.

Data Quality & Market Data

Build validation and quality checks for agricultural and market data.
Identify missing, duplicate, inconsistent, stale, or anomalous data.
Monitor data accuracy and consistency across different sources.
Investigate data-quality issues and identify their root causes.
Build workflows that improve the reliability of data used by analytics and AI systems.
Support data workflows for commodity prices, market trends, supply, demand, weather, and related market information.
Process historical and current market data across crops, markets, regions, and time periods.

AI-Ready Data & Retrieval

Prepare structured data, metadata, and documents for AI and LLM-powered applications.
Build workflows for schema discovery, metadata retrieval, and relevant data selection.
Help AI applications understand agricultural terminology, metrics, datasets, and relationships.
Build retrieval workflows for structured and unstructured agricultural information.
Work with embeddings, vector databases, metadata filtering, and hybrid retrieval where appropriate.
Improve retrieval relevance and data grounding for AI applications.
Evaluate retrieval quality and investigate incomplete, outdated, or irrelevant context.

NL-to-SQL & AI Data Workflows

Support natural-language-to-SQL and AI-powered data analysis workflows.
Prepare database schemas and metadata so AI systems can generate more accurate queries.
Develop data-access patterns that allow AI applications to retrieve relevant Farmioc data safely.
Support validation of AI-generated SQL and data-analysis outputs.
Apply appropriate access controls and read-only or restricted database access where required.
Work with engineers to improve the accuracy, reliability, and efficiency of AI-powered data workflows.

Analytics & ML Data Support

Prepare reliable datasets for analytics, machine learning, and predictive workflows.
Support feature preparation and historical data processing for model development.
Build and maintain data inputs required by forecasting and predictive analytics systems.
Ensure training and inference datasets are consistent and appropriately validated.
Collaborate with relevant engineering and analytics stakeholders on data requirements for models and analytical applications.

Performance, Reliability & Collaboration

Identify bottlenecks in data pipelines, queries, storage, and retrieval workflows.
Optimize data processing and database operations for performance and reliability.
Implement appropriate error handling, retries, validation, and recovery mechanisms.
Troubleshoot production data issues and contribute to root-cause analysis.
Work with backend, AI, product, and engineering teams to translate requirements into practical data solutions.
Contribute to technical design, testing, documentation, code reviews, and production releases.
Build reusable data capabilities that can support multiple Farmioc products.

What We're Looking For

3–5 years of experience in data engineering, software engineering, AI engineering, or a related field.
Strong Python and SQL programming skills.
Experience building data pipelines and data integration workflows.
Strong understanding of relational databases and data modeling.
Experience working with structured and semi-structured data.
Experience with data validation, quality checks, and troubleshooting.
Experience working with APIs and data services.
Familiarity with AI application workflows and data retrieval.
Understanding of embeddings, RAG, vector databases, or semantic search.
Strong debugging, analytical, and problem-solving skills.
Ability to work effectively with engineering and product teams.

Preferred Qualifications

Experience with PostgreSQL, MySQL, MongoDB, or other databases.
Experience with ETL/ELT pipelines and data-processing frameworks.
Experience with Pandas, NumPy, or similar Python data-processing libraries.
Experience with cloud and deployment platforms such as AWS, Azure, GCP, Render, or Vercel.
Experience building RAG, semantic search, or AI-powered retrieval workflows.
Experience with NL-to-SQL or AI-powered data analytics.
Familiarity with agricultural, commodity, market, weather, or geospatial datasets.
Experience supporting forecasting or predictive analytics data workflows.
Experience with Docker, CI/CD, automated testing, and production deployments.
Familiarity with data visualization or dashboard-based analytics.

Key Skills

What We Offer

Opportunity to work with real-world agricultural and commodity datasets.
Exposure to agricultural intelligence, market analytics, AI, and predictive analytics.
Opportunity to contribute to DataStore and other data-driven Farmioc products.
Experience working across data engineering, AI, analytics, and production systems.
Opportunity to solve practical data and technology problems across the agriculture value chain.

Why Join Farmioc

At Farmioc, you will work at the intersection of agricultural data, AI, analytics, and commodity intelligence. You will help build the data foundations that enable reliable analysis, market intelligence, retrieval, and AI-powered decision support across Farmioc's products.

Equal Opportunity

Farmioc is committed to providing equal employment opportunities and maintaining an inclusive and professional workplace.

Trida Network

Data & AI Engineer