AI / ML Engineer

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

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