Genai Engineer

**Title**:GenAI Engineer

**Job Type**:Full-Time

**Location**:Remote

**Company Description**:
**Job Summary**:
**Key Responsibilities**:

- Contribute to building and enhancing our Mechanized AI platform and AI-enabled
- products including mAI Modernize
- Serve as ML SME on client projects as needed
- Design ML systems
- Research and implement appropriate ML algorithms and tools
- Select appropriate datasets and data representation methods
- Run ML tests and experiments
- Perform statistical analysis and fine-tuning using test results
- Train and retrain systems when necessary
- Extend existing ML libraries and frameworks
- Stay current with emerging technologies and ML best practices to continuously
- improve our methodologies and tools

**Required Skills & Experience**:

- 4+ years of ML experience at a start-up or larger enterprise - high priority
- Client delivery experience - high priority
- Effective written and oral communications skills (C1/C2 - advanced/proficient level English is required) - high priority
- Bachelor's degree in computer science, software engineering or related field
- Experience with cloud environments (e.g., AWS, Azure, GCP)
- Experience with ML frameworks and libraries (TensorFlow, PyTorch, Keras, scikit-learn)
- Experience developing, deploying, and managing/monitoring models
- Knowledge of containerization technologies (e.g., Docker, Kubernetes) and microservices architecture
- Expertise in Object-Oriented Programming (OOP) principles and unit test-driven development methodologies
- Strong proficiency in Python programming
- Familiarity with prompt engineering approaches and best practices
- Knowledge of data structures, data modeling, and software architecture
- Strong analytical and problem-solving skills, with ability to propose innovative solutions and troubleshoot issues
- Ability to work independently and as part of a collaborative team in a fast-paced environment

**Preferred Qualifications**:

- Experience in any of the following:

- Agent development
- Data privacy
- Fine tuning LLMs
- LLM architecture and techniques for performance
- MLOps
- ML evaluation
- Model decay and data drift detection and handling
- Pulumi, Terraform, and/or Cloud SDKs
- PySpark
- Quantization
- Retrieval-augmented generation (RAG) optimization
- Security
- Vector databases

**Contact**:
Gabriel Martinez
Recruiter
Mechanized AI