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
• 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