What are the responsibilities and job description for the SR AI/ML ENGINEER position at TriosysIT Inc.?
Job Details
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
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, Google Cloud Platform, or OCI for deploying and managing ML
workloads. We leverage AI/ML tools across all major cloud providers (Azure AI, AWS
SageMaker/Bedrock, Google Cloud Platform 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
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 | Google Cloud Platform Vertex AI | OCI AI
Services
Infrastructure: Docker | Kubernetes | AWS/Azure/Google Cloud Platform/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
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) you have the most production experience in
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.