AI ML Engineer
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Job Location:
Austin, TX - USA
Monthly Salary:
$ 40 - 40
Posted on:
3 hours ago
Vacancies:
1 Vacancy
Job Summary
About the Role
Were looking for a sharp fast-moving AI/ML engineer who thrives in ambiguity and gets excited about building
things from scratch. Youll be tackling greenfield projects across various ML domains - whether thats NLP
time series forecasting recommendation systems or computer vision.
The path isnt always clear and your ability to think on your feet and problem-solve in real-time will be critical.
This isnt 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 Youll Build
AI/ML systems from the ground up - youll 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 youre comfortable in vim/neovim and live in the CLI youll 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 - youre comfortable (and prefer) working in the terminal
Fast thinker - you can rapidly assess problems prototype solutions and iterate
Problem solver - you dont need the answer handed to you; you figure it out
Greenfield-ready - youre 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 Were 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 youve built
2. Which 2-3 ML domains (from our list) do you have the most production experience in
Required Skills:
AI ML Engineer
Required Education:
AI ML Engineer
Company Industry
IT Services and IT Consulting
Key Skills
- ASP.NET
- Health Education
- Fashion Designing
- Fiber
- Investigation
