Artificial Intelligence Engineer - Data Infrastructure

Description : AI Applications Engineer Annotation Tooling and Data Operations.

About KGeN/KAI :

KGeNs KAI Training & Evaluation business taps a network of 1M+ verified experts across 60+ countries and 20+ languages, delivering specialised datasets across domains such as coding, STEM, healthcare, content, design, legal and general data collection and labeling power large?language and multimodal AI systems.

KGeN is one the fastest growing startups with revenue growth to $50M+ ARR within 3 years.

Funded by the most marquee investors Accel, Prosus, Sequoia, Nexus and Lightspeed.

Role Overview :

You will design, develop, and deploy intelligent tooling and systems that enhance the efficiency of KAIs GenAI operations.

Your work will focus on automating human-in-the-loop (HITL) pipelines, improving data labeling and evaluation workflows, and leveraging AI/ML to optimize annotation quality, routing, and workforce productivity.

This role is ideal for engineers passionate about combining applied ML, backend systems, and data infrastructure to improve real-world AI training at scale.

Key Responsibilities :

- Build the Multimodal AI Annotation Platform.

- Architect and develop a modular annotation system supporting images, audio, video, and text.

- Integrate open-source frameworks (Label Studio, CVAT, Diffgram, Whisper, Pyannote, MediaPipe, Rekognition, Vertex AI, etc.) with custom logic for quality control, automation, and AI-assist.

- Implement APIs and SDKs to manage data ingestion (S3/GCS), task creation, and result exports.

- Build a human-in-the-loop QC pipeline combining auto-validation and reviewer workflows.

- Create and maintain ML backend services for AI-assisted labeling using existing or custom models.

- Build Products that Convert Customer AI Needs into Solutions.

- Work directly with customers (internal/external) to translate AI training or labeling needs into product workflows using the annotation platform.

- Define ontology templates, QC workflows, and model integration plans for each customer.

- Prototype AI-assisted workflows quickly and iterate based on feedback.

- Manage the end-to-end lifecycle: requirements ? design ? implementation ? validation ? delivery.

- Integrate AI and MLOps Stack.

- Connect annotation outputs with downstream model pipelines (training, fine-tuning, retraining).

- Integrate evaluation metrics and feedback loops into product dashboards.

- Automate end-to-end flow from data labeling ? model training ? validation ? retraining.

- Optimize compute, cost, and throughput across AI workloads.

Required Skills and Experience :

Category :

Requirements :

Experience :

- 4-8 years of experience in full-stack or AI application development, with 2+ years in ML or AI-powered systems.

Programming :

- Strong in Python, Node.js, and cloud-native development (FastAPI, Flask, Express).

AI Tools :

- Experience integrating vision, audio, and NLP models (AWS Rekognition, OpenAI Whisper, Pyannote, Hugging Face Transformers, Vertex AI, etc.

Data Annotation / QC Tools :

- Hands-on with Label Studio, CVAT, Diffgram, or similar OSS platforms; experience extending them via API/SDK.

Cloud Platforms :

- Proficient in AWS or GCP (S3/GCS, Lambda/Cloud Functions, API Gateway, Cloud Run).

Front-end :

- Familiarity with React / Next.js / Retool / Streamlit for internal dashboards.

Databases :

- PostgreSQL, DynamoDB, or Firestore; experience designing schema for annotation/QC metadata.

DevOps :

- Docker, CI/CD, environment management, API versioning.

AI Systems Thinking :

- Ability to design scalable, multimodal pipelines combining automation + human review.

Soft Skills :

- Ownership, clarity in communication, user empathy, ability to lead cross-functional efforts.

Nice-to-Have Skills :

- Knowledge of MLOps orchestration tools (Airflow, Kubeflow, Prefect).

- Experience with active learning / data curation loops.

- Background in AI model evaluation or data quality metrics.

- Exposure to Reinforcement Learning from Human Feedback (RLHF) or LLM fine-tuning datasets.

- Prior experience building or managing an internal AI labeling workforce or QC automation system.

Who You Are :

- Youre an engineer who loves building full systems, not just models from UI to cloud to model integration.

- You have a strong product sense you think in terms of user experience, feedback loops, and measurable outcomes.

- Youre hands-on and autonomous able to ship production-quality code while owning design and strategy.

- You thrive in a startup-style environment where you define structure, not follow it.

What we offer :

- Opportunity to work at the cutting?edge of GenAI training and evaluation for multimodal AI systems.

- Opportunity to directly work with global AI clients.

- Competitive salary + performance incentives.

- Fast tracked career growth in a rapidly expanding business within KGeN.