LLM Engineer - Artificial Intelligence/Machine Learning System

Role Overview :

We are seeking a highly skilled LLM Engineer with strong expertise in building, deploying, and optimizing Large Language Model (LLM)-driven applications.

This role demands a professional who can integrate LLM capabilities into scalable backend systems while also collaborating on frontend delivery.

Key Responsibilities :

LLM Development & Integration :

- Design, fine-tune, and deploy LLM-based solutions using frameworks such as AWS Bedrock, Gemini, Hugging Face, or OpenAI APIs.

- Build custom pipelines for prompt engineering, retrieval-augmented generation (RAG), and model orchestration.

- Optimize model inference performance and reduce latency in real-world deployments.

Backend Engineering :

scalability, modularity, and performance.

- Implement APIs and microservices to integrate AI-driven features with enterprise-grade applications.

- Work with EKS, Docker, and Kubernetes for containerized deployments and orchestration.

Vector Database & Retrieval Systems :

- Design and implement embeddings pipelines to support RAG (Retrieval-Augmented Generation) use cases.

- Ensure efficient storage, indexing, and retrieval of unstructured data for LLM consumption.

Cloud & Infrastructure :

- Implement cloud-native best practices for security, cost optimization, and monitoring.

- Automate deployment pipelines (CI/CD) for ML-powered services.

Frontend Integration :

- Ensure seamless user experiences for AI-driven interfaces, including conversational UIs and intelligent dashboards.

Cross-Functional Collaboration :

- Contribute to system architecture, scalability discussions, and performance reviews.

- Stay updated with the latest advancements in LLMs, vector search, and AI tooling.

Required Skills & Qualifications :

- LLM Expertise : Proven hands-on work with AWS Bedrock, Gemini, Hugging Face, or similar LLM ecosystems.

- Programming Skills : Strong proficiency in Python for backend development, API design, and AI integration.

- Frameworks & Tools : Experience with FastAPI, Flask, and containerized deployments (EKS, Docker, Kubernetes).

- Vector Databases : Practical experience with Pinecone, FAISS, Weaviate, or similar technologies.

- Cloud Proficiency : Hands-on exposure to AWS and GCP services for deploying and scaling LLM applications.

- Frontend Knowledge : Working experience with React, Vue.js, or Lovable.dev for AI feature integration.

- System Design : Ability to design scalable, distributed, and fault-tolerant AI-driven architectures.

- Problem-Solving : Strong debugging, optimization, and performance tuning skills