Key Responsibilities
- Apply strong fundamentals and academic knowledge of Machine Learning algorithms and statistics to real-world problems.
- Work with medium to large-scale datasets, including time-series, anomaly detection, text, and image data.
- Conduct exploratory data analysis (EDA), curate high-quality datasets, and perform error analysis for ML and software algorithms.
- Design and implement ML solutions for Computer Vision, NLP, LLMs, and VLMs.
- Follow a methodical approach: formulate hypotheses, prototype models, and validate performance using measurable KPIs.
- Leverage GenAI and Agentic AI advancements in projects.
- Collaborate in international teams/projects, maintaining strong communication and documentation standards.
- Apply MLOps best practices, ensuring reproducibility, scalability, and deployment readiness.
- Use Docker and other containerization/orchestration tools for ML workflows.
- Align ML solutions with business objectives, ensuring strong business acumen in problem-solving.
Required Skills
- Machine Learning Expertise: Hands-on experience with frameworks such as Scikit-learn, TensorFlow, PyTorch.
- NLP & LLMs: Strong understanding of text embeddings, transformer-based models (e.g., BERT, RoBERTa, GPT, Hugging Face Transformers).
- Vector Search & Similarity Algorithms: Proficiency in FAISS, Milvus, Pinecone, and knowledge of cosine similarity, dot-product scoring, clustering methods.
- Programming: Strong Python skills with NumPy, Pandas, Scikit-learn, and ML/AI libraries.
- Version Control: Proficiency with Git and collaborative coding practices.
- Cloud & DevOps: Familiarity with Azure (preferred), Docker, and Kubernetes.
- Analytical Mindset: Curious, research-driven, with the ability to transition from hypothesis to validated ML solutions.
- Documentation & Communication: Ability to produce high-quality documentation and work effectively in cross-functional teams.
Cynosure Corporate Solutions
