Python AI Engineer Required for LLM Deployment
Budget: $30 – $250 USD
Tired of boring, soul-crushing jobs? So are we.
We're not just another AI shop – we're a people-first business on a mission to solve real-world problems, from removing CO₂ from the atmosphere to building tools that actually matter. And we want you to grow with us – not for 7 days, but for years.
This gig starts as a part‑time 7‑10 day project, but if you're the right fit, we're keeping you long‑term. We need a Python AI Engineer who wants to build something epic, have fun, and be part of a team that actually cares.
Still with us? Good. Here's what we're building:
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PYTHON AI ENGINEER – PART-TIME (7–10 DAY PROJECT)
Long-term role for the right person.
We are deploying a full AI ecosystem with LLMs, memory systems, search, video generation, and creative tools. We need a Python AI Engineer to deploy the AI core on production infrastructure.
This is a PRODUCTION deployment role – NOT research.
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WHAT YOU'LL DO:
· Deploy vLLM inference servers on RunPod GPU pods (Llama 3.3 70B, Qwen 2.5 Coder 32B, DeepSeek R1 32B, BGE-large)
· Set up OpenAI-compatible API endpoints, VRAM management, Flash Attention, health checks
· Build Memory Service (FastAPI) – embeddings with BGE, vector storage with Qdrant, similarity search
· Build Brain Gateway (FastAPI) – router for LLM requests, orchestration, context retrieval, session management
· Build Scraping Worker – web scraping, text chunking, async processing with NATS
· Set up RAG pipeline with Qdrant + Typesense (hybrid search)
· Deploy FLUX / Playground inference + Whisper.cpp for subtitles + Video Engine API
· Containerise all services with Docker, integrate with Kong API Gateway
· Set up logging (Loki), metrics (Prometheus), tracing (Tempo)
· Write API docs (Swagger) and deployment runbook
· Train the Full Stack team on using the AI APIs
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REQUIREMENTS (Must Have):
· Python (5+ years professional)
· FastAPI or Flask (expert)
· vLLM or TensorRT-LLM (production deployment)
· RunPod, AWS SageMaker, or similar GPU cloud
· Docker & Docker Compose (advanced)
· Vector DBs (Qdrant, Pinecone, or Weaviate)
· PostgreSQL with pgvector
· Redis (caching + queue)
· Git & CI/CD
· Linux admin (Ubuntu, shell scripting)
Nice to have:
Llama/Qwen/DeepSeek experience, AWQ/GPTQ quantisation, NATS, Kong, Vault, Prometheus/Grafana/Loki/Tempo, MinIO/S3, Whisper, FLUX/Stable Diffusion.
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HOW TO APPLY:
Send your application to
Include:
· CV/Resume highlighting Python AI production experience
· GitHub/Portfolio with AI deployment projects
· Brief proposal on how you'd approach this
· References from previous clients/employers
Subject Line: "Python AI Engineer - [Your Name]"
---
IMPORTANT:
· Do NOT apply if you only have Jupyter/R&D experience – this is production.
· Do NOT apply if you cannot work with GPUs and CUDA.
· Do NOT apply if you cannot commit to the 7–10 day timeline.
· Be ready to start immediately.
· Long-term work available – if you're good, we keep you.
· Location: Lucknow, India (remote OK).
We're not just another AI shop – we're a people-first business on a mission to solve real-world problems, from removing CO₂ from the atmosphere to building tools that actually matter. And we want you to grow with us – not for 7 days, but for years.
This gig starts as a part‑time 7‑10 day project, but if you're the right fit, we're keeping you long‑term. We need a Python AI Engineer who wants to build something epic, have fun, and be part of a team that actually cares.
Still with us? Good. Here's what we're building:
---
PYTHON AI ENGINEER – PART-TIME (7–10 DAY PROJECT)
Long-term role for the right person.
We are deploying a full AI ecosystem with LLMs, memory systems, search, video generation, and creative tools. We need a Python AI Engineer to deploy the AI core on production infrastructure.
This is a PRODUCTION deployment role – NOT research.
---
WHAT YOU'LL DO:
· Deploy vLLM inference servers on RunPod GPU pods (Llama 3.3 70B, Qwen 2.5 Coder 32B, DeepSeek R1 32B, BGE-large)
· Set up OpenAI-compatible API endpoints, VRAM management, Flash Attention, health checks
· Build Memory Service (FastAPI) – embeddings with BGE, vector storage with Qdrant, similarity search
· Build Brain Gateway (FastAPI) – router for LLM requests, orchestration, context retrieval, session management
· Build Scraping Worker – web scraping, text chunking, async processing with NATS
· Set up RAG pipeline with Qdrant + Typesense (hybrid search)
· Deploy FLUX / Playground inference + Whisper.cpp for subtitles + Video Engine API
· Containerise all services with Docker, integrate with Kong API Gateway
· Set up logging (Loki), metrics (Prometheus), tracing (Tempo)
· Write API docs (Swagger) and deployment runbook
· Train the Full Stack team on using the AI APIs
---
REQUIREMENTS (Must Have):
· Python (5+ years professional)
· FastAPI or Flask (expert)
· vLLM or TensorRT-LLM (production deployment)
· RunPod, AWS SageMaker, or similar GPU cloud
· Docker & Docker Compose (advanced)
· Vector DBs (Qdrant, Pinecone, or Weaviate)
· PostgreSQL with pgvector
· Redis (caching + queue)
· Git & CI/CD
· Linux admin (Ubuntu, shell scripting)
Nice to have:
Llama/Qwen/DeepSeek experience, AWQ/GPTQ quantisation, NATS, Kong, Vault, Prometheus/Grafana/Loki/Tempo, MinIO/S3, Whisper, FLUX/Stable Diffusion.
---
HOW TO APPLY:
Send your application to
Include:
· CV/Resume highlighting Python AI production experience
· GitHub/Portfolio with AI deployment projects
· Brief proposal on how you'd approach this
· References from previous clients/employers
Subject Line: "Python AI Engineer - [Your Name]"
---
IMPORTANT:
· Do NOT apply if you only have Jupyter/R&D experience – this is production.
· Do NOT apply if you cannot work with GPUs and CUDA.
· Do NOT apply if you cannot commit to the 7–10 day timeline.
· Be ready to start immediately.
· Long-term work available – if you're good, we keep you.
· Location: Lucknow, India (remote OK).
Related categories:
Python
Graphic Design
Software Architecture
Git
Docker
AWS SageMaker
FastAPI
AI Development