Data Scientist

Applied Scientist / ML Engineer (Search & Recommendations) We are looking for a highly skilled Applied Scientist / Machine Learning Engineer to lead the innovation and development of our next-generation Search and Recommendation systems. The ideal candidate will have deep expertise in classical ML, Deep Learning, NLP, and advanced Transformer-based architectures, including BERT and modern Large Language Models (LLMs).

Key Responsibilities

Search & Recommendation Development

Lead the end-to-end design, development, and deployment of search, personalization, and recommendation algorithms.

Build systems that significantly enhance user experience and drive measurable business impact.

Transformer-Based Model Implementation

Apply, fine-tune, and optimize models such as BERT, RoBERTa, and other encoder architectures for :

Semantic search

Relevance ranking

Query understanding

Embedding generation

Large Language Model (LLM) Innovation

Research, prototype, and implement solutions using LLMs.

Work on model selection, prompt engineering, LoRA-based fine-tuning, and quantization for efficient inference.

Design and implement RAG (Retrieval-Augmented Generation) systems using vector databases and advanced retrieval pipelines.

ML Productionization (MLOps)

Build, train, validate, and deploy machine learning models into scalable, low-latency production environments.

Collaborate with engineering teams to ensure reliability, robustness, and maintainability.

Data Strategy & Feature Engineering

Partner with Data Engineering to define datasets and develop innovative features for training and evaluation.

Ensure data quality and consistency across search and recommendation pipelines.

Evaluation & Optimization

Define and track KPIs such as NDCG, CTR, latency, perplexity, and other model metrics.

Continuously iterate to improve model performance and system quality.

Essential Technical Qualifications

MS / PhD in Computer Science, Data Science, Engineering, or equivalent experience.

• Expert-level Python skills;

strong knowledge of ML / DL libraries (NumPy, Pandas, etc.) and solid software engineering practices.

Deep experience with PyTorch or TensorFlow .

Proven hands-on work with Transformer models (BERT, encoder-only models) for IR, NLU, or embedding generation.

Practical experience with LLMs , including fine-tuning, deployment, and familiarity with frameworks such as Hugging Face, LangChain, and LlamaIndex.

Strong foundational understanding of classical ML algorithms and statistical modeling.

Direct experience building or optimizing search ranking systems , recommendation engines, dense retrieval, or vector-based search.

Experience with cloud platforms (AWS, GCP, Azure) and MLOps tools such as MLFlow, Kubeflow, Docker, Kubernetes.