Senior Data Scientist – Model AI & LLM Systems

Requirements:

1) Large Language Models (LLMs) & Agentic AI -

Hands-on experience designing and productionizing LLM-based systems beyond chat

use cases.

Strong expertise in prompt engineering for structured outputs (JSON, schemas, APIs,

models).

Experience building multi-agent / agentic workflows (planner, reasoning, validation,

execution).

Familiarity with tool-calling, function calling, and agent orchestration frameworks.

Ability to ground LLMs using RAG, metadata stores, and domain knowledge.

2) Machine Learning & NLP Foundations -

Strong grounding in NLP concepts (embeddings, similarity search, classification).

Experience designing confidence scoring, ranking, and ambiguity detection mechanisms.

Ability to evaluate and benchmark model outputs using custom metrics and test sets.

Understanding of model bias, hallucination risks, and mitigation techniques.

3) Explainability, Validation & Governance -

Experience building explainable AI outputs suitable for enterprise and governance use

cases.

Ability to combine rule-based validation with LLM reasoning.

Understanding of auditability, lineage, and reproducibility in AI-driven systems.

Good-to-Have:

Experience with Data Vault 2.0 concepts (Hubs, Links, Satellites).

Knowledge of Knowledge Graphs for entity and relationship grounding.

Exposure to telecom or large-scale enterprise data domains.

Integrate with:

1) Metadata systems (catalogs, schemas, lineage)

2) Transformation engines (dbt, SQL, Spark)

3) Quality frameworks (Great Expectations or similar)

Collaborate on RAG + Knowledge Graph architectures for grounding LLM outputs.

Optimize latency, cost, and reliability of LLM pipelines in production.