AI / ML Engineer

Job Description

About the Role

We're looking for a sharp, fast-moving AI/ML engineer who thrives in ambiguity and gets excited about building things from scratch. You'll be tackling greenfield projects across various ML domains - whether that's NLP, time series forecasting, recommendation systems, or computer vision. The path isn't always clear, and your ability to think on your feet and problem-solve in real-time will be critical. This isn't a role for someone who needs detailed specs and hand-holding. We need someone who can figure it out, move fast, and ship production-quality code.

What You'll Build

• AI/ML systems from the ground up - you'll own projects from conception to production
  
• Scalable ML pipelines and data workflows
  
• Production-grade models serving real users at scale
  
• MLOps infrastructure for training, deployment, and monitoring
  
• Internal tooling that makes the team more efficient
  
• Work primarily in the terminal - if you're comfortable in vim/neovim and live in the CLI, you'll fit right in

Required Skills

Core Technical (Non-Negotiable)

• Python - 3-5+ years production experience, this is your primary language
  
• AI/ML Production - Built and deployed 2-3+ ML models serving real users, not just experiments
  
• Cloud Platforms - Experience with AWS, Azure, GCP, or OCI for deploying and managing ML workloads. We leverage AI/ML tools across all major cloud providers (Azure AI, AWS SageMaker/Bedrock, GCP Vertex AI, OCI AI Services)
  
• DevOps - Docker and Kubernetes experience
  
• Databases - SQL (PostgreSQL, MySQL) and NoSQL/vector databases
  
• Scripting - Proficient in both Bash and PowerShell for automation

ML Domains (Must have strong experience in at least 2-3 of these)

• NLP/LLMs: Experience with transformers (BERT, GPT, T5), RAG systems, fine-tuning, prompt engineering, or building LLM applications
  
• Time Series: Forecasting models, anomaly detection, sequential data modeling, or real-time monitoring systems
  
• Recommender Systems: Collaborative filtering, ranking models, personalization engines, or content recommendations
  
• MLOps Tools: Production experience with MLflow, Weights & Biases, Kubeflow, Airflow, or similar platforms
  
• Distributed Training: Large-scale model training, multi-GPU/multi-node setups, efficient data parallelism

Working Style (Critical)

• CLI-first developer - you're comfortable (and prefer) working in the terminal
  
• Fast thinker - you can rapidly assess problems, prototype solutions, and iterate
  
• Problem solver - you don't need the answer handed to you; you figure it out
  
• Greenfield-ready - you're energized by building new things, not just maintaining existing systems
  
• Self-directed - you can take ambiguous requirements and turn them into working solutions

Nice to Have

• CI/CD Experience: Azure DevOps, GitHub Actions, Jenkins, or similar automation pipelines
  
• Computer Vision: Production CV experience with PyTorch/TensorFlow, OpenCV, object detection, segmentation, or real-time inference
  
• Additional Languages: Go or Rust experience for performance-critical components
  
• Feature stores (Feast, Tecton) or advanced feature engineering
  
• Model optimization: quantization, pruning, knowledge distillation
  
• Edge deployment or resource-constrained model deployment
  
• Experiment frameworks for A/B testing ML models
  
• Contributions to open-source ML projects
  
• Real-time streaming data processing (Kafka, Kinesis)

What We're NOT Looking For

• Someone who needs extensive documentation before starting
  
• Developers who only work with GUIs
  
• People uncomfortable with ambiguity or rapid change
  
• Engineers who need constant direction
  
• Junior developers still learning ML fundamentals

Our Stack

Core: Python | PyTorch/TensorFlow | Scikit-learn | FastAPI/Flask | Git | Bash/PowerShell

ML/AI Tools: MLflow | Airflow/Kubeflow | Azure AI | AWS SageMaker/Bedrock | GCP Vertex AI | OCI AI Services

Infrastructure: Docker | Kubernetes | AWS/Azure/GCP/OCI | PostgreSQL | Azure DevOps | GitHub Actions

Experience Level

• 3-5+ years in AI/ML engineering roles
  
• Proven track record of shipping 0-to-1 ML projects
  
• Production ML experience (not just research or coursework)

Why Join Us

• Real impact: Your work directly affects our product and users
  
• Technical freedom: Choose your tools, own your decisions
  
• Fast feedback loops: See your code in production within days, not months
  
• No red tape: Small team, direct access to leadership
  
• Cutting edge: Work with latest ML/AI tech, not maintaining legacy systems
  
• Growth: Own entire ML systems end-to-end and influence technical direction

Interview Process

1. Quick call (30 min) - culture fit, basic technical discussion
   
2. Technical challenge (take-home, 2-3 hours) - build something real
   
3. Live problem solving (60 min) - work through a realistic ML problem together
   
4. Team meet (30 min) - meet potential teammates

To Apply: Send your resume and briefly describe:**

1. The most challenging greenfield ML project you've built
   
2. Which 2-3 ML domains (from our list) do you have the most production experience in.