Overview
We are seeking a highly skilled Full Stack AI Engineer to design, build, and scale intelligent applications across the full technology stack. This role combines strong backend and frontend engineering expertise with applied AI/ML implementation in enterprise cloud environments.
You will work closely with product managers, architects, data scientists, and DevOps teams to deliver production-grade AI-powered solutions that are secure, scalable, and aligned with business objectives.
This is a hands-on engineering role requiring experience across application development, AI model integration, cloud architecture, and DevSecOps practices.
Job Description
Key Responsibilities
AI / Machine Learning:
Design and implement AI/ML solutions for real-world business use cases.
Integrate ML models (e.g., forecasting, classification, NLP, computer vision) into production-grade applications.
Develop APIs and services that expose AI capabilities securely and efficiently.
Optimize model performance, latency, scalability, and monitoring in production.
Implement model lifecycle management (training, deployment, monitoring, retraining).
Backend Development:
Design and develop scalable RESTful and/or GraphQL APIs.
Build microservices-based architectures.
Implement authentication, authorization, and secure API access.
Develop data pipelines and integrate with structured and unstructured data sources.
Ensure high availability, performance tuning, and observability.
Frontend Development:
Develop responsive, user-friendly web applications.
Build interactive dashboards and AI-driven user experiences.
Integrate frontend applications with backend AI services.
Ensure accessibility, usability, and performance optimization.
Cloud & DevOps:
Deploy applications and models in cloud environments (Azure, AWS, or GCP).
Implement CI/CD pipelines for application and model deployment.
Apply infrastructure-as-code (Terraform, ARM, Bicep, etc.).
Implement monitoring, logging, and alerting.
Ensure security compliance and enterprise-grade governance.
Architecture & Collaboration:
Participate in solution architecture and design discussions.
Translate business requirements into technical solutions.
Collaborate with cross-functional teams (product, security, data, UX).
Contribute to technical standards, best practices, and code reviews.
Required Qualifications
5+ years of full stack software engineering experience.
2+ years of hands-on AI/ML implementation in production environments.
Strong proficiency in:
oPython (FastAPI, Flask, or Django)
oJavaScript/TypeScript (React, Angular, or Vue)
oREST API development
Experience with ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow, or equivalent).
Experience deploying AI workloads in cloud environments (Azure ML, SageMaker, Vertex AI, etc.).
Experience with relational and NoSQL databases.
Strong understanding of software engineering principles and design patterns.
Experience with Docker and container orchestration (Kubernetes preferred).
Knowledge of secure coding practices and enterprise security standards.
Preferred Qualifications
Experience with enterprise AI governance and responsible AI frameworks.
Experience with MLOps and model monitoring tools.
Knowledge of distributed systems and event-driven architectures.
Experience with vector databases and semantic search (if applicable to organization).
Experience in regulated industries (financial services, healthcare, public sector).
Experience working in Agile/Scrum teams.
Key Competencies
Strong problem-solving and analytical skills.
Ability to translate complex AI concepts into scalable technical solutions.
Excellent communication and stakeholder engagement skills.
Ownership mindset with the ability to operate independently.
Strong attention to performance, security, and maintainability.
Skills & Requirements
Python, FastAPI, Flask, Django, JavaScript, TypeScript, React, Angular, Vue, REST API, GraphQL, AI/ML, Machine Learning, Scikit-Learn, PyTorch, TensorFlow, NLP, Computer Vision, Forecasting, Classification, MLOps, AI Model Integration, Azure ML, AWS SageMaker, Google Vertex AI, AWS, Azure, GCP, Docker, Kubernetes, Terraform, ARM, Bicep, CI/CD, Microservices, Data Pipelines, Relational Databases, NoSQL Databases, Vector Databases, Semantic Search, Event-Driven Architecture, Distributed Systems, Authentication, Authorization, Secure Coding, DevSecOps, Model Monitoring, Model Lifecycle Management, Cloud Architecture, Infrastructure As Code, Agile, Scrum