Senior Full-Stack Developer & AI Engineer 

About the Client

Our client is a well-established platform delivering career intelligence, employer insights, and professional development resources to a global audience. As part of its continued growth, the company is investing in next-generation internal and external systems focused on communication, marketing, workflow automation, and intelligent integrations — with a strong emphasis on scalability, reliability, modern architecture, and AI-driven innovation.

This is a rare opportunity to join a high-impact engineering team where software architecture and artificial intelligence converge to create systems that not only function reliably, but also learn, adapt, and optimize outcomes in real time.

Overview

We are seeking a highly experienced Senior Full-Stack Developer & AI Engineer to lead the design, development, and deployment of intelligent, production-grade systems.

This is not a traditional software engineering role. You will own both:

1. Application Layer

(UI, backend systems, APIs, infrastructure, integrations)

2. Intelligence Layer

(AI models, machine learning pipelines, LLM systems, adaptive decision engines)

You will architect and build complete end-to-end platforms that combine scalable software engineering with production AI capabilities.

Key Responsibilities:

End-to-End Intelligent System Ownership

• Design and deliver complete intelligent systems across full stack: UI → APIs → Database → Integrations → AI pipelines
• Own architecture, implementation, deployment, and long-term maintainability
• Build systems that automate real-world decision-making and adapt over time
• Ensure reliability, scalability, performance, and resilience in production

Full-Stack Development

• Design and build scalable production-grade applications end-to-end
• Develop modern frontend applications using: React Angular
• Build backend services using: Node.js Python .NET
• Develop REST and GraphQL APIs
• Implement workflow engines and RBAC authorization systems
• Design scalable PostgreSQL schemas, including JSONB hybrid data models
• Build secure authentication and enterprise-grade access controls

AI / Machine Learning Engineering

Design, deploy, and optimize AI systems in production, including:

Machine Learning:

• Classification models
• Regression models
• Clustering systems
• Recommendation engines

Deep Learning:

• Neural networks
• Transformer architectures

LLM Systems:

• Prompt engineering
• Fine-tuning workflows
• Embeddings pipelines
• Retrieval-Augmented Generation (RAG)

AI Deployment:

• Build scalable inference systems optimized for: Latency Cost Throughput Accuracy  AI System Design & Data Engineering

• Design end-to-end feedback learning loops
• Build retraining pipelines and model refresh systems
• Implement drift detection and accuracy monitoring
• Perform feature engineering and dataset versioning
• Process structured + unstructured datasets
• Build ETL / ELT pipelines for AI-ready data flows

Intelligent Solution Development

Design and build systems such as:

• AI-powered workflow automation platforms
• Claims intelligence engines
• Risk scoring and decision support systems
• Natural language interfaces and AI assistants
• Intelligent anomaly detection systems

 Architecture & System Design

• Define scalable service boundaries and architecture patterns
• Design integration patterns: REST GraphQL Event-driven architectures Anticipate scale bottlenecks and failure modes
• Make clear architectural trade-offs balancing speed, cost, and maintainability

Infrastructure & DevOps

• Deploy and manage systems in AWS or Azure
• Manage containerized workloads with Docker + Kubernetes
• Build and maintain CI/CD pipelines
• Implement:
  • Logging
  • Monitoring
  • Tracing
  • Alerting
• Ensure high-availability and fault-tolerant production systems

Production Ownership

• Own uptime, performance, and reliability of deployed systems
• Troubleshoot production incidents across app + AI stack
• Design retry logic, idempotency, and fault tolerance patterns
• Optimize cloud infrastructure and inference costs

Engineering Leadership

• Establish coding, testing, and deployment standards
• Conduct architecture reviews and code reviews
• Mentor junior and mid-level engineers
• Prevent technical debt through scalable engineering decisions

Cross-Functional Collaboration

• Translate business requirements into robust technical systems
• Partner with product, business, and leadership stakeholders
• Align AI capabilities directly with business workflows and outcomes

Required Qualifications

Experience

• 7+ years in full-stack software engineering
• Proven experience designing scalable production systems
• Proven experience deploying AI/ML systems into production

Full-Stack Engineering

Strong hands-on expertise in:

• React or Angular
• Node.js, Python, or .NET
• PostgreSQL (JSONB strongly preferred)
• REST / GraphQL APIs
• AWS or Azure
• RDS / Aurora PostgreSQL

AI / Machine Learning

Strong production experience with:

• Python ML ecosystem
• TensorFlow and/or PyTorch
• Scikit-learn
• Pandas

Experience with:

• LLM architectures
• Embeddings
• Vector databases
• Prompt engineering
• RAG pipelines

System Design

Proven ability to:

• Design end-to-end systems (application + data + AI)
• Build scalable intelligent platforms
• Optimize systems for latency, cost, and performance

Infrastructure

Hands-on experience with:

• Docker
• Kubernetes
• CI/CD pipelines
• Monitoring and observability tooling

Security & Compliance

• Strong understanding of secure system design
• Familiarity with HIPAA-level or regulated environments
• Experience with enterprise security ecosystems (Microsoft / Google environments preferred)

Nice to Have

• Spark experience
• Event-driven / distributed architecture expertise
• Experience with anomaly detection systems
• Experience in workflow automation products
• Exposure to regulated industries such as healthcare, insurance, or finance

What Success Looks Like

In this role, you will:

• Design and launch intelligent production systems end-to-end
• Integrate AI directly into business workflows
• Reduce system complexity by unifying:
  • Software Engineering
  • Data Science
  • ML Operations
• Deliver faster, more scalable, more adaptive platforms

Why This Role Matters

This role consolidates multiple disciplines into one execution layer, enabling:

• Faster delivery cycles
• Reduced coordination overhead
• More cohesive architecture
• Smarter systems that continuously improve

Ideal Candidate

You are a builder who can:

• Architect robust software platforms
• Deploy AI systems in production
• Create systems that learn and adapt over time
• Balance engineering rigor with business impact

You think beyond features — and build intelligent ecosystems.

Work Setup

• Fully remote distributed team (US + international)
• Partial overlap required: PST 8:00 AM – 11:00 AM
• High-ownership, low-bureaucracy environment