Artificial Intelligence (AI) Engineer id-7532
Description
Location: San Diego, CA
Type: FTE/Contract
Interview mode: F2F
Description
As a Machine Learning Engineer on our core Software AIML team, you will be at the forefront of designing and developing cutting-edge GenAI applications.
You will be working closely with business stakeholders and data engineers to communicate AI recommendations to senior management.
Key Responsibilities:
- Engage deeply with business teams to identify opportunities and translate the needs into innovative and practical AIML solutions
- Design, build, and deploy state of the art AIML models to solve complex business problems
- Understand key performance levers and metrics to highlight AIML solution operational issues and drive improvement
- Foster close collaboration with engineers and infrastructure partners to implement robust and scalable solutions
- Communicate the results and insights effectively to partners and senior leaders, providing clear and actionable recommendations
- Stay updated with the latest trends, technologies, and best practices in AI and GenAI and data engineering
- Regularly research and present new ideas to improve the team's technical capabilities.
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Minimum Qualifications:
- Industry experience 10+ years BSCS, 5+ years MS/PhD in Computer Science, Statistics, Applied Math, or a related field
- Preferred Qualifications:
- Expert in AIML modeling, Python, AIML Infrastructure, model deployment and MLOps
- Experience working in Supply Chain, Operations, or a related field
- End to end GenAI application experience
- Knowledge in RAG, Prompt Engineering, LoRA and other GenAI techniques
- Ability to operate independently and lead without authority
- Superb communication and interpersonal skills
AI SW Engineer Skills/Experience:
- Experience in AI System Development foundational model Lifecycle training, retrieval of Augmented Systems, and Model Evaluation.
- Proficient in Large Language Models (LLMs) Architecture, creating targeted Instruction Datasets, and implementing Quantization and Inference Optimizations.
- Capable of covering Problem Definition, Data Acquisition, and Full-cycle Deployment, utilizing tools like MLflow, Cloud Platforms like AWS, Google Cloud, Azure.
- Proprietary Models like GPT-4 and Claude, Open-source Models like Llama and Mistral, able to deliver high-performing, scalable, and cost-efficient AI solutions.
- NLP libraries like NLTK, spaCy, GPT-3, BERT, and Hugging Face's Transformers to create sophisticated natural language processing solutions.
TECHNICAL SKILLS:
Methodologies: Spiral, Agile, Waterfall, Lean
Programming Language: C++, Python, Shell, SQL, R
IDE Tools: PyCharm, RStudio, Visual Studio Code, Jupyter Notebook, Google Collab, Navicat
Client Frameworks: Transformers, Scikit-Learn, Keras, TensorFlow, PyTorch, ONNX, NLTK, OpenAI, langchain,
llama-index,kore.ai
DL Architectures: LLM, ANN, CNN, R-CNN, RNN, GRU, LSTM, Transformers, Attention Mechanism, Tokenisers,
BERT, T5, Sentence Transformers, Foundational models
Packages: Pandas, NumPy, Spark, Matplotlib, SciPy
Cloud Technologies: AWS (EC2, S3, RDS, ECS, Lambda), Azure(VM, Functions, ACR, AKS), Digital Ocean, GCP, Paperspace
Database: MySQL, PostgreSQL, MongoDB, ChromaDB, Pinecone, SQLite
Web Frameworks and Web Servers/ Deployment:
Fast API, Flask, Nginx, Gunicorn, Uvicorn, Docker, Kubernetes
Miscellaneous Tools and Technologies:
JIRA, Auto Gluon, GitHub Actions, Github Issues, Datadog, MS Power BI, Postman, Locust, Stream lit, CUDA, cuDNN, TensorRT
Version Control: Git, GitHub, DVC (Data Version Control)
Operating Systems: Windows, Linux, Mac
