AI Engineer - Mobile & Web Application Development

Company: UNOAH AI Ltd.

Job Title: AI Engineer (Full-Stack AI Systems, Infrastructure & Architecture)

Location: Mangalore / Bangalore, Karnataka (On-site)

Employment Type: Full-Time

Joining: Immediate

About UNOAH

UNOAH AI Ltd. is a faith-based, AI-driven technology company building digital products and services where technology and AI serve meaningful human and spiritual journeys. This role will support the development of AI-powered products across UNOAH's technology verticals.

Role Summary

We are seeking an AI Engineer who can design, build, deploy, and operate AI/ML-powered features within web and mobile applications end-to-end, from model development and system architecture through cloud infrastructure, DevOps, and secure payment integrations. This role goes beyond model-building: it requires ownership of the full technical stack that AI features run on, including infrastructure management, solution architecture, and production reliability.

The ideal candidate is comfortable moving between designing an AI/ML solution, architecting the system it lives in, provisioning and securing the cloud infrastructure that runs it, and integrating payment or third-party services around it.

Key Roles & Responsibilities

1. AI & Generative AI Solution Development

· Design and develop AI/ML models based on business and product requirements.

· Build Generative AI solutions using Large Language Models (LLMs), including chatbots, AI assistants, document Q&A; systems, and RAG (Retrieval-Augmented Generation) pipelines.

· Apply prompt engineering best practices to optimize LLM outputs for accuracy, safety, and cost.

· Integrate AI capabilities such as recommendation engines, computer vision, speech recognition, and predictive analytics into web and mobile applications.

· Develop intelligent automation workflows connecting AI models to business processes.

2. Machine Learning

· Prepare, clean, and engineer datasets for training and evaluation.

· Train, evaluate, fine-tune, and optimize ML models for accuracy, latency, and cost.

· Build classification, recommendation, and predictive systems.

· Establish feedback loops for continuous learning and model improvement.

3. System Architecture & Solution Design

· Design end-to-end system architecture for AI-powered mobile and web applications across client, API, model-serving, data, and infrastructure layers.

· Define solution designs covering scalability, fault tolerance, data flow, caching, and cost efficiency.

· Make build-vs-buy and platform decisions, including self-hosted models versus managed LLM APIs, vector database selection, and hosting topology.

· Produce architecture diagrams, technical specifications,



and design documents for engineering and stakeholder review.

· Ensure architecture supports versioning, rollback, and safe iteration of AI models in production.

4. Integration & APIs

· Develop and maintain REST APIs to expose AI services to web and mobile clients.

· Integrate third-party LLM platforms including OpenAI, Azure OpenAI, Google Gemini, Anthropic Claude, and others.

· Integrate payment gateways such as Stripe, Razorpay, PayPal, Braintree, or equivalent for AI-driven features involving billing, usage-based pricing, subscriptions, or in-app purchases.

· Optimize API and model response latency for real-time and near-real-time use cases.

· Ensure secure, well-documented, versioned API contracts between AI services and application front ends.

5. Cloud Infrastructure & AWS Management

· Provision, configure, and manage AWS infrastructure including EC2, S3, Lambda, SageMaker, RDS, API Gateway, ECS/EKS, CloudFront, IAM, and VPC supporting AI workloads.

· Manage compute resources for model training and inference, balancing cost against performance through spot instances, autoscaling, and GPU provisioning.

· Configure networking, security groups, and IAM roles/policies following least-privilege principles.

· Monitor cloud spend and optimize resource allocation for cost efficiency.

· Maintain infrastructure-as-code using Terraform, CloudFormation, or CDK for reproducible environments.

6. DevOps & System Infrastructure Management

· Build and maintain CI/CD pipelines using GitHub Actions or equivalent for model and application deployment.

· Containerize AI services using Docker and orchestrate with Kubernetes for scalable deployment.

· Manage deployment environments (dev, staging, production) and infrastructure health.

· Implement observability through logging, metrics, alerting, and dashboards for model and system performance.

· Own incident response for AI/infrastructure outages, including root-cause analysis and remediation.

· Manage database infrastructure including PostgreSQL, MongoDB, and MySQL supporting AI application data.

7. Deployment & Monitoring

· Deploy AI models to production on cloud platforms, with AWS as primary and Azure/GCP as needed.

· Monitor live model accuracy, drift, and performance and set up automated alerts for degradation.

· Retrain and redeploy models as needed based on performance data and new data availability.





· Conduct A/B testing and canary releases for new model versions.

8. Security & AI Governance

· Ensure secure handling of AI training data, user data, and model outputs.

· Protect sensitive information, including PII and payment data, in line with applicable data protection requirements such as GDPR and PCI-DSS where payment data is involved.

· Implement AI governance policies including access controls, audit logging, model usage policies, and responsible-AI guardrails.

· Secure API keys, secrets, and credentials using proper secrets-management tooling such as AWS Secrets Manager, Vault, or equivalent.

Required Qualifications

- Bachelor's/Master's degree in Computer Science, AI/ML, Software Engineering, or a related field, or equivalent practical experience.
- Proven experience building and deploying AI/ML features in production web or mobile applications.
- Hands-on experience with cloud infrastructure management, ideally AWS.
- Experience integrating at least one payment gateway into a production application.
- Solid understanding of system architecture and solution design for distributed applications.
- Robust DevOps fundamentals including CI/CD, containerization, and orchestration.

Tools & Technologies

- Programming Languages: Python, JavaScript/TypeScript
- AI/ML Frameworks: TensorFlow, PyTorch, Scikit-learn, Hugging Face Transformers
- LLM Platforms: OpenAI, Azure OpenAI, Google Gemini, Anthropic Claude
- GenAI Frameworks: LangChain, LlamaIndex
- Vector Databases: Pinecone, ChromaDB, FAISS, Weaviate
- API Frameworks: FastAPI, Flask, Node.js/Express (for integration layers)
- Cloud Platforms: AWS (primary: EC2, S3, Lambda, SageMaker, RDS, ECS/EKS, API Gateway, CloudFront, IAM), Azure, Google Cloud
- Infrastructure as Code: Terraform, AWS CloudFormation/CDK
- DevOps & Orchestration: Docker, Kubernetes, GitHub Actions, Jenkins (optional)
- Databases: PostgreSQL, MongoDB, MySQL
- Payment Gateways: Stripe, Razorpay, PayPal, Braintree (or equivalent)
- Monitoring & Observability: CloudWatch, Prometheus, Grafana, Datadog (any of these)

Preferred / Nice to Have

- Experience with GPU infrastructure and cost optimization for model training/inference.
- Familiarity with mobile app deployment pipelines (iOS/Android) and how AI services integrate with them.
- Experience with PCI-DSS compliant payment flows.
- Prior experience setting up AI governance or model-risk frameworks in a regulated environment.

Compensation

INR 50,000 per month.

How to Apply

Send your CV, clearly mentioning the position applied for, to:

Shambhavi Jha | AI-HCM Executive, HR
[email protected]

Shortlisted candidates will be contacted for the next stage of the selection process.

Pay: From ₹50,000.00 per month

Work Location: In person

📌 AI Engineer | Mobile & Web Application Development (Mangalore)
🏢 UNOAH AI
📍 Mangalore