About the role

Role: Python Developer (AI & LLMs)

Build end-to-end AI solutions centered on LLMs, RAG, and graph-based knowledge.

Responsibilities

AI & LLM Systems
- Build RAG (Retrieval-Augmented Generation) pipelines for context-aware responses.
- Implement and fine-tune vLLM for efficient LLM inference.
- Collaborate with ML engineers to deploy transformer models (e.g., BERT, GPT variants) and work with vector databases.
Data & Database Architecture
- Architect and optimize Neo4j (graph database) systems for modeling knowledge networks and relationships.
Microservices & Integrations
- Develop Python microservices for ingestion, processing, and API integrations using FastAPI and Flask.
Performance & Operations
- Monitor performance, run A/B tests, and ensure low-latency production responses.
- Ensure scalability and efficiency of AI systems.

Requirements

Proficiency in Python and AI/ML libraries such as PyTorch, TensorFlow, and Hugging Face Transformers.
Hands-on experience with graph databases, especially Neo4j (including Cypher queries and graph algorithms).
Proven work building RAG pipelines (retrieval, reranking, generation) using LangChain or LlamaIndex.
Experience with vLLM (or similar LLM optimization tooling, e.g., quantization/distributed inference).
Knowledge of vector databases like FAISS and Pinecone, plus embedding techniques.
Familiarity with cloud platforms (AWS/GCP/Azure) and containerization (Docker, Kubernetes).

Nice-to-haves

Strong experience with FastAPI or Flask for high-performance APIs.
Familiarity with MLOps practices and tools (MLflow, Kubeflow).
Contributions to open-source AI/ML projects.
Experience tuning performance and running A/B testing in production AI environments.

Soft Skills

Strong analytical and problem-solving skills.
Excellent communication and collaboration.
Self-motivated; able to work independently and as part of a team.

Offer

Competitive salary with performance-based bonuses.
Flexible working hours with remote work options.
Professional development opportunities; inclusive work environment.
Paid sick time and paid time off; Provident Fund; yearly bonus.

About SPACE AI

SPACE AI builds AI systems and applications, with a focus on modern large language model (LLM) and retrieval-augmented generation (RAG) capabilities. The role emphasizes developing production-grade AI pipelines, deploying transformer models, and building supporting data and graph infrastructure.

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