Requirements

Expected technologies

LLAMA

Deepseek

Mistral

Python

Operating system

Linux

Our requirements

• Education:
• Bachelor’s/Master’s/PhD in Computer Science, AI, or related field
• Experience:
• 3+ years in ML/AI roles, with 2+ years focused on RAG systems.
• Proven experience deploying LLMs in on-prem or hybrid environments.
• Proficiency with vLLM, LiteLLM, and open-source LLMs (e.g., LLAMA, Deepseek, Mistral).
• Experience in introducing AI Agents/Assistants
• Technical Skills:
• Strong Python expertise with frameworks like PyTorch, Hugging Face Transformers, and LangChain.
• Experience with vector/graph databases (e.g. Neo4j).
• Familiarity with Linux-based systems and RedHat OpenShift
• Soft Skills:
• Ability to communicate complex AI concepts to non-technical stakeholders.
• Strong problem-solving skills and adaptability in fast-paced environments.

Your responsibilities

• RAG System Development:
• Architect and deploy end-to-end RAG pipelines, combining retrieval mechanisms (e.g., vector databases like Neo4j) with generative models (e.g., LLAMA) for enterprise use cases.
• Fine-tune and optimize retrieval models to ensure high accuracy and low latency in on-prem environments.
• Model Integration & Deployment:
• Implement and customize inference servers using vLLM for efficient LLM serving and LiteLLM for lightweight model orchestration.
• Integrate open-source LLMs (e.g., LLAMA, Mistral) with proprietary data sources and APIs.
• On-Prem Infrastructure Management:
• Design GPU-optimized, scalable infrastructure for LLM training and inference, ensuring compliance with security and data governance policies.
• Collaborate with DevOps teams to containerize workflows using Docker/Kubernetes and automate MLOps pipelines.
• Performance Optimization:
• Apply techniques like quantization, pruning, and dynamic batching to maximize resource efficiency in resource-constrained on-prem setups.
• Monitor system performance, troubleshoot bottlenecks, and ensure high availability.
• Cross-Functional Collaboration:
• Partner with data engineers to curate and preprocess domain-specific datasets for retrieval and generation tasks.
• Translate business requirements into technical solutions for stakeholders in telco environments.