Data Science Engineer

Roles & responsibilities

• Serve as the

  AI & data strategist

  , discovering and onboarding new data sources and shaping end-to-end pipelines that span traditional analytics,

  machine learning (ML), deep learning (DL), natural-language processing (NLP), generative AI (Gen AI), and agentic AI

  .
• Partner with engineering to design, build, and harden

  production-grade data and AI products

  (e.g., RAG search, LLM-powered assistants, anomaly-detection services).
• Run analytical experiments classification, forecasting, LLM fine-tuning, prompt-engineering A/B tests to solve business problems across multiple domains.
• Collect, clean, and enrich large

  structured, semi-structured, and unstructured

  datasets (text, image, audio, sensor, graph).
• Design, train, and validate

  classical ML models, DL architectures (CNN, RNN, Transformers), and instruction-tuned LLMs

  ; measure drift and conduct error analysis to drive iterative improvement.
• Implement

  retrieval-augmented generation

  and

  agentic task-planning

  pipelines that combine vector search, function calling, and tool invocation.
• Document findings, publish reusable components, and mentor junior data scientists and ML engineers.

Qualifications

Experience

• Proven experience in

  delivering data-driven solutions, with demonstrable depth in at least two of: ML, DL, NLP, GenAI, agentic AI.
• Hands-on proficiency in

  Python

  (pandas, NumPy, scikit-learn),

  DL frameworks

  (TensorFlow or PyTorch), and

  LLM / GenAI toolkits

  (Hugging Face Transformers, LangChain/LangGraph, Google ADK).
• Proven track record in

  feature engineering, pattern recognition, predictive modeling, and LLM fine-tuning / evaluation

  .
• Experience optimizing

  GPU / TPU

  workloads and scaling distributed training or inference.
• Familiarity with

  vector databases

  (FAISS, Chroma, Pinecone) and distributed data systems (Spark, BigQuery, Snowflake).

  sibilities

Preferred candidate profile