1. Role Purpose

Intelletto.ai is an AI “sidecar” platform that attaches to existing enterprise systems (ATS, HCM, TRM, CRM, ERP) and injects explainable intelligence — resume parsing and scoring, virtual automated recruitment (AVAR), workforce and customer intelligence, and closed-loop learning tied to 30/90/180-day outcomes.

The Principal Developer is the technical anchor who turns this vision into production-grade code. You will own the hardest parts of the platform: high-volume parsing and enrichment services, agentic AI workflows, GraphQL federation boundaries, secure integrations to customer systems, and the reusable UI patterns that all Intelletto modules share. You are not a people manager by title, but you will set patterns that other developers, AI engineers, and frontend teams follow.

2. Mission of the Role

Make Intelletto.ai shippable at scale — turn proof-of-concept stacks (resume scoring, AVAR, sidecar dashboards) into hardened, observable, multi-tenant services.
Codify the “sidecar” architecture — ensure every feature can attach beside an existing system without forcing the customer to re-platform.
Lead by example in code — write the reference implementations for ingestion, vector search, scoring pipelines, and UI shells that other teams can copy.
Guard performance and explainability — every AI feature must be observable, auditable, and explain why it ranked a candidate or triggered an insight.

3. Key Responsibilities

A. Architecture & Core Services

Design and implement core Intelletto.ai services: resume ingestion → parsing → enrichment → scoring → publish.
Define and maintain GraphQL federation schemas that sit in front of microservices (resume, candidate, AVAR, HRIS, customer intelligence).
Build secure, multi-tenant API layers that can connect to ATS/TRM/HCM/CRM systems without exposing customer data improperly.
Shape the domain models for candidate, requisition, scoring, AI-execution logs, and audit events so they can be reused across modules.

B. AI / Data Enablement

Integrate with AI providers (e.g. AWS Bedrock) and build wrappers that make LLM calls safe, cost-aware, and traceable.
Work with AI engineering to embed vector search (OpenSearch / k-NN) and semantic enrichment into the candidate and requisition flows.
Implement explainability hooks so downstream UIs can show “why this candidate” using stored features, not just opaque model output.

C. Productization & Developer Experience

Produce reference code for other teams: service templates, pipeline definitions, CI/CD patterns, and API best practices.
Enforce coding, logging, and observability standards (structured logs, trace IDs, metrics).
Partner with frontend to expose consistent JSON shapes to all Intelletto dashboards and HTML shells.

D. Integration & Security

Build and harden connectors to customer systems (ATS, HRIS, TOP/TRM, CRM) using secure OAuth2/OpenID flows and role-based access.
Work with IAM (Keycloak-style) to enforce tenant, role, and data-scope constraints in every endpoint.
Review threat surfaces for agentic/automated actions (auto time-in/out, auto-shortlist, auto-enrich) and ensure safe defaults.

E. Technical Leadership

Perform design reviews and code reviews for complex features.
Coach senior/full-stack developers on applying the Intelletto.ai architecture, especially around sidecar attachment and multi-system writes.
Help prioritise tech debt remediation when performance, readability, or security is at risk.

4. Outcomes / What Success Looks Like

90 days: Core parsing/enrichment/scoring services are running in a repeatable way; GraphQL gateway exposes clean schema to dashboards; logging is structured and traceable.
180 days: At least two Intelletto modules (e.g. Resume Scoring Allocator and AVAR) are using the same pipeline patterns; integrations to at least two external enterprise systems are live and secured.
12 months: The platform can onboard a new customer with a different ATS/HCM with configuration and adapters — not a rewrite.

5. Required Skills & Experience

A. Technical (Singapore Skills Framework-aligned)

Applications Development (Web & Services): 8–10+ years in Node.js/TypeScript or Java/Spring Boot/Kotlin.
Software Architecture & Solution Design for HR/recruitment/CRM-style, workflow-heavy B2B platforms.
Applications Integration / API Development: REST, GraphQL, webhooks, partner integrations.
Data & Information Management: entity design for candidate, job, scoring, audit, vector metadata with proper indexing.
Digital Fluency / Cloud: AWS (Lambda, API Gateway, ECS/EKS, serverless); infra-as-code a plus.
Security & Identity Enablement: RBAC, JWT, OAuth2/OIDC, tenant isolation, API key management.

B. Enterprise / Platform Experience

Experience with HR-tech / ATS / recruitment / talent-intelligence / CRM products.
Experience with search + scoring (OpenSearch/Elasticsearch, BM25, vector search).
Experience with LLM/GenAI integration (Bedrock, OpenAI, Anthropic) with guardrails and telemetry.

6. Soft & Hard Skills

Hard / Technical Skills

GraphQL federation design and resolver optimisation.
Event-driven / streaming architectures (Kafka).
RESTful API lifecycle (versioning, schema evolution, security).
Microservice observability: structured logging, metrics, tracing.
CI/CD pipeline definition and release management.
Containerisation (Docker) and deployment to AWS runtimes.
Secure integration patterns to third-party HR/ATS/HCM.
Data modelling for AI features (storing features, scores, rationales).

Soft / Behavioural Skills

Clear, structured communication for mixed technical audiences.
Collaboration with AI engineering, frontend, and integration teams.
Problem solving / innovation: ability to compare toolchains and pick fit-for-purpose stacks.
Service orientation: builds features that “sit beside” existing customer systems.
Mentoring and coaching for senior engineers.
Learning agility and R&D mindset for emerging AI features.

7. Nice-to-Have Skills

Exposure to Singapore Skills Framework / SkillsFuture-style skill taxonomies or structured skills ontology.
Experience building multi-tenant SaaS with customer-specific data controls.
Frontend familiarity (React, Vue, or HTML shells) for dashboard collaboration.
Knowledge of async pipelines for high-volume resume/event ingestion.

8. Education & Certifications

Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field.
Equivalent professional experience in large-scale, integration-heavy systems will be considered.
AWS, security, or cloud-native certifications are advantageous (e.g. AWS Developer / Solutions Architect).
Evidence of continuous learning in AI/ML, vector search, or modern integration patterns is a plus.

9. Why Join Intelletto.ai

You build the intelligence beside the intelligence — a platform that lets enterprises get AI value without ripping out their ATS/HCM.
You work close to the founder/CTO, so architecture and platform decisions are fast and visible.
You will ship real AI/agentic features into HR/recruitment workflows — not just demos.
You will define patterns that later teams in engineering, AI, and integrations will follow.