- 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.