Job Description

We are seeking a Junior Data Scientist with strong interest in Agentic AI (LLM-based systems, multi-agent workflows, and tool-augmented intelligence) to join our team. This role combines core data science, generative AI development, and exposure to enterprise AI
platforms such as Azure and Databricks. The candidate will contribute to building intelligent agents that support business use cases including decision intelligence, automated reporting, knowledge retrieval, and workflow automation.

Responsibilities:

Agentic AI Development (Corporate Main-Function Use Cases):
• Build task-driven agents that plan, call tools, and complete end-to-end workflows (e.g., self-serve analytics, automated reporting, request triage).
• Design RAG and knowledge-agent solutions for corporate functions (e.g., Policy/HR Q&A, SOP search, IT support knowledge, finance controls guidance) with strong grounding and citation patterns.
• Integrate agents with enterprise tools and data sources (SQL, APIs, ticketing, document repositories) and implement safe action execution (validation, approvals, and audit logs).
• Prototype and maintain multi-agent orchestration (planner/worker/critic patterns), human-in-the-loop handoffs, and escalation paths to SMEs.

Data Science & Machine Learning:
• Perform data cleaning, preprocessing, and exploratory data analysis
• Build and evaluate machine learning models
• Apply statistical and predictive techniques to business problems

Deployment & Engineering Support:
• Assist in deploying models and agents into production environments
• Work with REST APIs and basic containerized environments
• Support integration into enterprise platforms
Cloud & Data Platform (Preferred Exposure):
• Work with Microsoft Azure cloud services
• Utilize Databricks for data pipelines and scalable processing

Experimentation & Evaluation:
• Evaluate agent and LLM performance (task success rate, grounding quality, latency, and cost) using offline test sets and scenario-based simulations.
• Implement prompt/tooling guardrails (policy checks, PII handling, refusal patterns), and support basic monitoring/observability for agent runs.

Documentation & Collaboration:
• Document agent design (tools, permissions, prompts, evaluation results) and create runbooks for deployment, monitoring, and incident response.
• Partner with corporate function stakeholders (HR, Finance, Legal, Procurement, IT) to translate use cases into agent requirements, acceptance criteria, and measurable outcomes.

Continuous Learning:
• Stay updated on Generative AI, agent architectures, and MLOps practices

Qualifications:

Required:
• Bachelor’s degree in Computer Science, Data Science, Statistics, or related field
• 3–5 years of experience in data science or related role
• Strong Python skills (pandas, numpy, scikit-learn, langchain, langgraph)
• Understanding of machine learning fundamentals and data pipelines

Preferred:
• Experience with LLMs / Generative AI and agent frameworks
• Familiarity with RAG systems (vector databases, embeddings)
• Exposure to Microsoft Azure services
• Experience with Databricks (Spark, MLflow)
• Basic knowledge of APIs, Git, and Agile development

Key Skills:
• Data Science: EDA, machine learning, statistics
• AI/LLM: Prompting, RAG, embeddings
• Engineering: Python, APIs, deployment basics
• Platforms: Azure (preferred), Databricks (plus)
• Soft Skills: Communication, curiosity, problem-solving