AI Engineer

Job Brief

We are looking for an AI Engineer with a strong backend foundation and an AI-First development mindset. You will be responsible for designing, developing, and deploying production-grade AI systems. Unlike traditional roles where AI is an "add-on," you will treat AI as the primary operating model—architecting systems that are natively intelligent, adaptive, and scalable.

Key Responsibilities

• AI-First Backend Development: Build robust, scalable backend services using Python (FastAPI/Flask/Django) that serve as the backbone for AI-driven applications.
• Model Implementation & Fine-tuning: Work deeply with Transformers and LLMs (GPT-4, Claude, Llama 3, Mistral). Fine-tune open-source models for specific domain tasks using frameworks like Hugging Face, PyTorch, or TensorFlow.
• Advanced RAG Pipelines: Design and optimize Retrieval-Augmented Generation (RAG) systems, implementing sophisticated retrieval strategies and integrating Vector Databases (Pinecone, Weaviate, Milvus, PGVector or Chroma).
• Agentic Workflows: Develop AI agents and multi-agent systems using frameworks like LangChain, LlamaIndex, Google ADK, BAML , Agno or CrewAI to automate complex reasoning tasks.
• Production MLOps: Own the end-to-end lifecycle of AI models, from experimentation to CI/CD deployment, monitoring for hallucinations, and optimizing for latency and cost.
• System Architecture: Design "AI-native" architectures that prioritize semantic search, embeddings, and context-aware data flows over traditional keyword-based logic.
• On-Prem Model Serving & Optimization: Deploy and manage open-source LLMs (Llama 3, Mistral, DeepSeek) using high-throughput serving engines like vLLM, SGLang, and Ollama.
  
  

Required Qualification & Technical Skills

• Python Mastery: 4+ years of professional Python experience, including deep knowledge of asynchronous programming (asyncio), type hinting, and high-performance backend patterns.
• AI & NLP Core: Proven experience working with Transformer architectures, attention mechanisms, and tokenization.
• Vector Infrastructure: Hands-on experience with vector embeddings and similarity search optimization.
• Data Engineering: Proficiency in building data pipelines for AI, including data cleaning, ingestion, and prompt engineering.
• DevOps/MLOps: Strong experience with Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure). Familiarity with GPU orchestration and model quantization techniques (GGUF, AWQ).
  
  

Soft Skills & "AI-First" Mindset

• Problem Solver: You don't just "plug in an API." You understand the underlying math and logic of why a model behaves a certain way.
• Iterative Builder: Comfortable with the non-deterministic nature of AI; you build with robust error-handling and fallback mechanisms.
• Communicator: Ability to explain complex AI trade-offs (e.g., latency vs. accuracy) to non-technical stakeholders.
  
  

Bonus Points

• Experience with LLM evaluation frameworks (RAGAS, DeepEval).
• Contributions to open-source AI libraries or research papers in NLP.
• Knowledge of Graph Databases (Neo4j) for GraphRAG implementations.
  
  

What We Look For Beyond Required Skills

In order to thrive at VentureDive, you

…are intellectually smart and curious

…have the passion for and take pride in your work

…deeply believe in VentureDive’s mission, vision, and values

…have a no-frills attitude

…are a collaborative team player

…are ethical and honest

Are you ready to put your ideas into products and solutions that will be used by millions?

You will find VentureDive to be a quick pace, high standards, fun and a rewarding place to work at. Not only will your work reach millions of users world-wide, you will also be rewarded with competitive salaries and benefits. If you think you have what it takes to be a VenDian, come join us ... we're having a ball!