Key Responsibilities
Design and build end-to-end AI workflows including synthetic dataset generation, data preprocessing, model development, deployment, and agent orchestration.
Design and implement LLM (Large Language Model) evaluation frameworks to assess new AI models, techniques, and parameters.
Process and analyze large-scale structured and unstructured data from databases, APIs, and develop machine learning models for time series forecasting, regression, and classification tasks.
Design and implement enterprise-grade RAG systems using LLMs, vector databases, GraphRA, G, and intelligent agents.
Optimize AI-user interactions through advanced prompt engineering and retrieval strategies.
Translate cutting-edge AI research into practical, scalable tools for internal and customer-facing applications and collaborate with global and cross-functional R&D teams to drive innovation across product lines.
Apply MLOps and LLMOps best practices to ensure robust model training, deployment, and monitoring pipelines.
BSc or MSc in Computer Science, Software Engineering, Data Science, or a related field.
3+ years of experience as a Data Scientist or ML Engineer.
Hands-on expertise with machine learning techniques: regression, classification, clustering, time series
Deep interest and foundational knowledge, including hands-on experience with:
Transfer learning & fine-tuning adapting large pre-trained models (like GPT, LLaMA, Mistral) to domain-specific tasks.
LLMs, Prompt Engineering, and Chain-of-thought (CoT) prompting
Designing MCP APIs, Agent,s and Multi-Agent Orchestration
Vector databases (RAG) and GraphRAG-based solutions
Models for Content Moderation
Familiarity with MLOps & LLMOps practices automating model training, testing, deployment, monitoring, and governance.
Strong proficiency in Python (or other relevant programming languages).
Excellent written and verbal communication skills in English.
