AI Platform Development Specialist
Budget: ₹75,000 – ₹150,000 INR
Role Purpose
We are building a high-integrity, AI-assisted intelligence platform that converts unstructured and semi-structured data into decision-support insights.
The platform operates under explicit scope, governance, and safety constraints, prioritising correctness, explainability, and auditability. This role owns the entire SDLC and technical lifecycle: system architecture, data engineering, machine learning integration, infrastructure deployment, and quality assurance.
Core Responsibilities – Platform & Architecture
• Design and own a multi-store architecture (relational DB, object storage, search, vector systems)
• Define and enforce clear source-of-truth boundaries
Database Design & Data Engineering
• Lead schema and data model design for entities, relationships, events, and provenance
• Model data as timelines and relationship graphs
• Implement data validation, reconciliation, and integrity checks
• Design and Enforce Data Hygiene and Completeness framework
Unstructured Data Processing
• Build batch/offline pipelines for documents, text, and transcripts
• Implement OCR, parsing, and layout-aware extraction
• Preserve provenance, confidence scores, and audit logs
Data Science & Machine Learning
• Integrate ML pipelines for extraction, classification, resolution, and similarity
• Ensure models are descriptive, explainable, versioned, and monitored
• Translate experimental data science into production systems
Backend Services & APIs
• Design APIs exposing synthesised, traceable outputs
• Integrate 3rd party APIs to interface with official (verified) data sources
• Enforce RBAC, logging, and misuse prevention
Backend Services & APIs
• WebApp (Next.js or equivalent)
• UI Kit (Material UI / Chakra or equivalent)
• PDF.js
• Sandboxed Output
DevOps & Infrastructure
• Provision infrastructure using Infrastructure-as-Code
• Containerise and deploy services using Docker and Kubernetes (or equivalent)
• Build CI/CD pipelines and manage environments
• Ensure systems are monitorable, fault tolerant and recoverable
Observability & Operations
• Implement metrics, logging, and alerting
• Automate monitoring for pipeline health, latency, and failures
•Break/Fix (CI/CD, Infrastructure etc)
Semi Basic/Intermediate Quality Assurance
• Implement unit, integration, and basic end-to-end tests
• Validate data integrity and ML output sanity
• Support UAT and acceptance sign-off
Required Experience
• 10+ years in platform/backend/data engineering
• Advanced Python
• Strong PostgreSQL and relational modelling
• Experience with search engines and ML pipelines
• Docker, Kubernetes, CI/CD experience
Non-Goals
• No real-time streaming systems
• No predictive or scoring systems
• No black-box AI outputs
Success Criteria (Timeline to be confirmed)
• Platform deployed via reproducible infrastructure
• End-to-end pipelines operational with observability
• QA coverage in place
• No architectural path for prohibited behaviours
We are building a high-integrity, AI-assisted intelligence platform that converts unstructured and semi-structured data into decision-support insights.
The platform operates under explicit scope, governance, and safety constraints, prioritising correctness, explainability, and auditability. This role owns the entire SDLC and technical lifecycle: system architecture, data engineering, machine learning integration, infrastructure deployment, and quality assurance.
Core Responsibilities – Platform & Architecture
• Design and own a multi-store architecture (relational DB, object storage, search, vector systems)
• Define and enforce clear source-of-truth boundaries
Database Design & Data Engineering
• Lead schema and data model design for entities, relationships, events, and provenance
• Model data as timelines and relationship graphs
• Implement data validation, reconciliation, and integrity checks
• Design and Enforce Data Hygiene and Completeness framework
Unstructured Data Processing
• Build batch/offline pipelines for documents, text, and transcripts
• Implement OCR, parsing, and layout-aware extraction
• Preserve provenance, confidence scores, and audit logs
Data Science & Machine Learning
• Integrate ML pipelines for extraction, classification, resolution, and similarity
• Ensure models are descriptive, explainable, versioned, and monitored
• Translate experimental data science into production systems
Backend Services & APIs
• Design APIs exposing synthesised, traceable outputs
• Integrate 3rd party APIs to interface with official (verified) data sources
• Enforce RBAC, logging, and misuse prevention
Backend Services & APIs
• WebApp (Next.js or equivalent)
• UI Kit (Material UI / Chakra or equivalent)
• PDF.js
• Sandboxed Output
DevOps & Infrastructure
• Provision infrastructure using Infrastructure-as-Code
• Containerise and deploy services using Docker and Kubernetes (or equivalent)
• Build CI/CD pipelines and manage environments
• Ensure systems are monitorable, fault tolerant and recoverable
Observability & Operations
• Implement metrics, logging, and alerting
• Automate monitoring for pipeline health, latency, and failures
•Break/Fix (CI/CD, Infrastructure etc)
Semi Basic/Intermediate Quality Assurance
• Implement unit, integration, and basic end-to-end tests
• Validate data integrity and ML output sanity
• Support UAT and acceptance sign-off
Required Experience
• 10+ years in platform/backend/data engineering
• Advanced Python
• Strong PostgreSQL and relational modelling
• Experience with search engines and ML pipelines
• Docker, Kubernetes, CI/CD experience
Non-Goals
• No real-time streaming systems
• No predictive or scoring systems
• No black-box AI outputs
Success Criteria (Timeline to be confirmed)
• Platform deployed via reproducible infrastructure
• End-to-end pipelines operational with observability
• QA coverage in place
• No architectural path for prohibited behaviours