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Full-Stack AI Engineer

What You’ll Own

AI Application Development

Build and deploy AI-powered applications using modern software engineering best practices.

Integrate LLMs and machine learning models into production environments.

Develop intelligent features including:

• AI chatbots
• Semantic search
• Document intelligence
• AI copilots
• Workflow automation

Build scalable APIs that expose AI capabilities to applications.

LLMs, RAG & AI Integration

Integrate models using:

• OpenAI
• Hugging Face
• PyTorch
• TensorFlow

Build Retrieval-Augmented Generation (RAG) pipelines.

Implement semantic search using vector databases including:

• Pinecone
• Weaviate
• FAISS
• ChromaDB

Optimize prompt engineering and inference workflows.

Monitor model accuracy, latency, and production performance.

Data Engineering & AI Pipelines

Build ETL pipelines for structured and unstructured data.

Automate:

• Data ingestion
• Cleaning
• Validation
• Versioning

Manage workflows using:

• Airflow
• Prefect
• Dagster

Work with cloud data warehouses including:

• BigQuery
• Snowflake
• Amazon Redshift

Optimize pipelines for scalability and cost efficiency.

Full-Stack Development

Build modern user interfaces using:

• React
• Next.js
• Vue.js

Develop scalable backend services using:

• Python
• FastAPI
• Flask
• Node.js

Build APIs that support high-performance AI workloads.

Ensure applications remain responsive, secure, and production-ready.

Infrastructure, DevOps & MLOps

Deploy applications using:

• Docker
• Kubernetes

Build CI/CD pipelines for both applications and AI models.

Monitor infrastructure using:

• MLflow
• Weights & Biases
• Datadog
• Prometheus

Improve:

• Inference latency
• Infrastructure reliability
• Deployment automation
• Cloud cost optimization

Security & Compliance

Build secure AI systems using modern authentication and authorization practices.

Protect sensitive business and customer data.

Support compliance with:

• GDPR
• HIPAA
• SOC 2

Implement API security, rate limiting, and access controls.

Must-Have Qualifications

Experience

3+ years of software engineering experience with exposure to AI/ML systems.

Experience building production AI applications.

Experience deploying machine learning models into production environments.

Core Skills

Strong proficiency in:

• Python
• JavaScript / TypeScript

Hands-on experience with:

• OpenAI APIs
• Hugging Face
• PyTorch
• TensorFlow

Experience building scalable REST APIs.

Front-end development experience with:

• React
• Next.js
• Vue.js

Strong SQL skills and experience working with cloud databases.

Experience using:

• Docker
• Kubernetes
• CI/CD pipelines

Familiarity with vector databases and AI inference services.

• Build and deploy AI-powered applications using modern software engineering best practices.
• Integrate LLMs and machine learning models into production environments.
• Develop intelligent features including: AI chatbots, Semantic search, Document intelligence, AI copilots, Workflow automation.
• Build scalable APIs that expose AI capabilities to applications.
• Integrate models using: OpenAI, Hugging Face, PyTorch, TensorFlow.
• Build Retrieval-Augmented Generation (RAG) pipelines.
• Implement semantic search using vector databases including: Pinecone, Weaviate, FAISS, ChromaDB.
• Optimize prompt engineering and inference workflows.
• Monitor model accuracy, latency, and production performance.
• Build ETL pipelines for structured and unstructured data.
• Automate: Data ingestion, Cleaning, Validation, Versioning.
• Manage workflows using: Airflow, Prefect, Dagster.
• Work with cloud data warehouses including: BigQuery, Snowflake, Amazon Redshift.
• Optimize pipelines for scalability and cost efficiency.
• Build modern user interfaces using: React, Next.js, Vue.js.
• Develop scalable backend services using: Python, FastAPI, Flask, Node.js.
• Build APIs that support high-performance AI workloads.
• Ensure applications remain responsive, secure, and production-ready.
• Deploy applications using: Docker, Kubernetes.
• Build CI/CD pipelines for both applications and AI models.
• Monitor infrastructure using: MLflow, Weights & Biases, Datadog, Prometheus.
• Improve: Inference latency, Infrastructure reliability, Deployment automation, Cloud cost optimization.
• Build secure AI systems using modern authentication and authorization practices.
• Protect sensitive business and customer data.
• Support compliance with: GDPR, HIPAA, SOC 2.
• Implement API security, rate limiting, and access controls.

• Python
• JavaScript / TypeScript
• OpenAI APIs
• Hugging Face
• PyTorch
• TensorFlow
• REST APIs
• React
• Next.js
• Vue.js
• SQL
• Docker
• Kubernetes
• CI/CD pipelines
• Vector databases
• AI inference services

• 3+ years of software engineering experience with exposure to AI/ML systems.
• Experience building production AI applications.
• Experience deploying machine learning models into production environments.
• Strong proficiency in Python.
• Strong proficiency in JavaScript / TypeScript.
• Hands-on experience with OpenAI APIs.
• Hands-on experience with Hugging Face.
• Hands-on experience with PyTorch.
• Hands-on experience with TensorFlow.
• Experience building scalable REST APIs.
• Front-end development experience with React.
• Front-end development experience with Next.js.
• Front-end development experience with Vue.js.
• Strong SQL skills and experience working with cloud databases.
• Experience using Docker.
• Experience using Kubernetes.
• Experience using CI/CD pipelines.
• Familiarity with vector databases and AI inference services.