• Deploy and integrate AI/ML models using OpenAI, Hugging Face, TensorFlow, PyTorch, or similar frameworks
• Build scalable APIs for AI inference using FastAPI, Flask, or Node.js
• Develop retrieval-augmented generation (RAG) pipelines using Pinecone, Weaviate, FAISS, or vector databases
• Implement embeddings, semantic search, and AI-powered workflows
• Optimize inference performance, latency, and cost efficiency
Full-Stack Application Development
• Build frontend interfaces using React, Next.js, Vue, or modern JavaScript frameworks
• Develop backend systems and APIs that connect AI models with business logic
• Create user-facing AI features such as chatbots, copilots, dashboards, and automation tools
• Ensure applications are responsive, secure, scalable, and production-ready
• Build microservices and scalable backend architectures
Data Engineering & Pipelines
• Develop ETL pipelines for ingesting, cleaning, transforming, and managing datasets
• Automate preprocessing, data labeling, and workflow orchestration using Airflow, Prefect, or Dagster
• Manage structured and unstructured datasets in cloud environments
• Maintain reliable pipelines for model training, fine-tuning, and evaluation
Infrastructure, DevOps & MLOps
• Containerize AI services using Docker and deploy applications using Kubernetes or cloud infrastructure
• Build CI/CD pipelines for model deployments and application releases
• Monitor model performance, drift, costs, and system reliability
• Work with cloud platforms such as AWS, GCP, Azure, Vertex AI, or SageMaker
• Improve scalability, uptime, and infrastructure efficiency
Security, Compliance & Reliability
• Implement secure API authentication, access control, and rate limiting
• Ensure AI systems comply with GDPR, HIPAA, SOC 2, or related compliance requirements
• Maintain monitoring, logging, and observability for production systems
• Troubleshoot production incidents and optimize system reliability
Collaboration & Product Development
• Partner with product and data teams to define AI-powered product features
• Translate AI prototypes into scalable production systems
• Participate in sprint planning, technical discussions, and architecture decisions
• Maintain clear technical documentation and reproducible workflows