Data Scientist

Location:

About Koantek:

we help them uncover new opportunities and achieve a competitive advantage in the digital age.

About the Role

Experience with Databricks (especially MLOps Stacks) is highly desirable.

Key Responsibilities :

• Translate business challenges into solvable NLP and GenAI use cases, such as
  document understanding, web search, automated Q&A, summarization, and workflow automation. Stay updated with the latest GenAI/LLM advancements and evaluate them for feasibility and potential use.
• Design, build, and deploy LLM-powered retrieval-augmented generation (RAG) pipelines and agentic AI solutions, including multi-step reasoning systems,tool-using agents, and associated pipelines.
• Build basic UI frontends (e.g., using Streamlit, Flask) for internal demos or
  client-facing pilot GenAI applications.
• Apply MLOps best practices including MLflow-based tracking, Docker containerization, and CI/CD for GenAI pipelines.
  Develop customer demos and prototypes using Databricks MosaicAI suite. Contribute to both internal R&D efforts and customer implementations, including rapid POCs and scalable production deployments.

Required Qualifications :

• 4-8 years of implementation experience in machine learning, with a strong focus on NLP and GenAI applications in a customer-facing role.
• Must have productionized machine learning or deep learning models. Familiarity with SQL and working with large, complex datasets.
• Proficiency in Python and NLP/LLM libraries/tools such as HuggingFace Transformers,
  LangChain, LangGraph, LlamaIndex, etc.
• Hands-on experience with prompt engineering, chunking, vector embeddings, semantic
  search, RAG pipelines, and LLM fine-tuning.

Understanding of GenAI-specific challenges - hallucination, prompt security, rate limits,
cost optimization, etc.

Strong foundation in statistics, including:

Evaluation metrics and error analysis

Probabilistic modeling, hypothesis testing, and uncertainty quantification
Feature importance and interpretability techniques Experience in MLOps tools and processes, including: Model versioning and experiment tracking (e.g., MLflow)

Containerization (Docker)

CI/CD for ML workflows (e.g., GitHub Actions, Azure DevOps, or similar)
Model monitoring and retraining workflows

Must: Hands-on experience with Databricks for model development and deployment. Must: Familiarity with cloud environments and the native AI/ML-related tools/services (Azure, AWS, or GCP).

Strong analytical and communication skills, with a demonstrated ability to convert business requirements into NLP/GenAI solutions.

Educational Background

Workplace Flexibility