Full-Stack AI Engineer

Full-Stack AI Engineer

Our client is seeking a highly skilled Full-Stack AI Engineer to design, build, and deploy scalable AI-powered applications that solve real-world business problems.

This role bridges software engineering with applied machine learning, combining front-end development, back-end systems, AI model integration, and cloud infrastructure into production-ready applications. You will work across the full product lifecycle — from experimentation and prototyping to deployment, optimization, and monitoring.

The ideal candidate is both technically strong and execution-focused, capable of building AI-driven systems that are scalable, reliable, performant, and user-friendly.

AI Model Integration & LLM Systems

• Deploy and integrate pre-trained and fine-tuned ML/LLM models using OpenAI, Hugging Face, TensorFlow, PyTorch, or similar frameworks

• Build scalable AI inference APIs using FastAPI, Flask, Node.js, or similar technologies

• Implement retrieval-augmented generation (RAG) pipelines using vector databases such as Pinecone, Weaviate, Chroma, or FAISS

• Optimize prompt engineering, embeddings, and AI workflows for performance, accuracy, and cost efficiency

Full-Stack Application Development

• Build responsive front-end applications using React, Next.js, Vue, or similar frameworks

• Develop back-end services and APIs connecting AI systems to business workflows and user-facing applications

• Design scalable architectures for chatbots, AI assistants, analytics dashboards, search systems, and workflow automation tools

• Ensure applications are intuitive, secure, responsive, and production-ready

Data Engineering & Pipeline Development

• Build ETL/ELT pipelines for ingesting, cleaning, transforming, and processing structured and unstructured datasets

• Automate data preprocessing, versioning, labeling, and pipeline orchestration using Airflow, Prefect, Dagster, or similar tools

• Store and manage datasets within cloud warehouses such as Snowflake, BigQuery, or Redshift

• Maintain reliable data flows supporting training, inference, analytics, and AI operations

Infrastructure, Deployment & MLOps

• Containerize AI services using Docker and deploy workloads to Kubernetes or cloud-native environments

• Build and maintain CI/CD pipelines for AI model updates and application releases

• Monitor inference latency, application performance, costs, and model drift using MLflow, Weights & Biases, Prometheus, or custom dashboards

• Support scalable and reliable cloud infrastructure on AWS, GCP, or Azure

Security & Compliance

• Ensure AI systems comply with GDPR, HIPAA, SOC 2, or relevant privacy/security standards

• Implement authentication, access control, rate limiting, and secure API practices

• Protect user data and AI workflows using modern security standards and best practices

Collaboration & Product Development

• Collaborate with product managers, designers, and data scientists to prioritize impactful AI features

• Translate prototypes into production-grade systems with scalable architecture and maintainable code

• Participate in sprint planning, architecture discussions, code reviews, and technical documentation

• Maintain clear documentation to support reproducibility, onboarding, and long-term maintainability

What Makes You a Perfect Fit

• Strong software engineer with deep curiosity around AI/ML systems and emerging technologies

• Comfortable moving quickly from prototype to production-grade deployment

• Analytical and solutions-oriented with strong debugging and optimization skills

• Able to balance performance, scalability, usability, and operational cost

• Collaborative communicator who works effectively across technical and non-technical teams

Required Experience & Skills

• 3+ years of professional software engineering experience with AI/ML exposure

• Strong proficiency in Python and JavaScript/TypeScript

• Experience with AI/ML frameworks such as PyTorch, TensorFlow, LangChain, or Hugging Face

• Experience deploying AI or ML models into production systems

• Strong front-end experience with React, Next.js, or Vue

• Strong SQL skills and experience with cloud data warehouses

• Familiarity with REST APIs, microservices, and distributed systems

• Experience with Docker, CI/CD workflows, and cloud infrastructure

Preferred Experience & Skills

• Experience building and scaling AI-powered SaaS applications

• Strong understanding of embeddings, vector databases, and RAG architectures

• Experience with LLM fine-tuning, evaluation, and prompt optimization

• Familiarity with MLOps tools such as MLflow, Kubeflow, Vertex AI, SageMaker, or Weights & Biases

• Experience with serverless architectures and cost-optimized inference systems

• Background in SaaS, automation platforms, analytics systems, or AI-driven products

What Does a Typical Day Look Like?

A Full-Stack AI Engineer's day revolves around transforming AI capabilities into scalable, production-ready applications. You will:

• Review and optimize AI model APIs for latency, accuracy, and reliability

• Build front-end interfaces that expose AI-driven functionality to end users

• Maintain and improve data pipelines supporting AI systems and analytics

• Deploy updates through CI/CD workflows and monitor production performance

• Collaborate with product and data science teams on AI feature prioritization

• Debug infrastructure, inference, or workflow issues impacting system performance

• Document architectures, workflows, and deployment processes for maintainability and scaling

In essence: you ensure AI systems move beyond prototypes into secure, scalable, reliable, and impactful production applications.

Key Metrics for Success (KPIs)

• Successful deployment of AI features aligned with sprint timelines

• Application uptime ≥ 99.9%

• Inference latency maintained below target thresholds

• Reduction in manual workflows through AI automation

• Stable model performance and minimized drift or degradation

• Positive adoption and engagement with AI-powered features

• Scalable, maintainable, and cost-efficient AI infrastructure

Interview Process

• Initial Phone Screen

• Video Interview with Recruiter

• Technical Assessment (e.g., deploy an ML model with API + front-end integration)

• Client Interview(s) with Engineering / Product Teams

• Offer & Background Verification