Senior Data Scientist

Aria Intelligent Solutions; Kolkata, New Delhi; Hybrid; INR 1,500,000 - 2,400,000; Full time

Job summary

Aria Intelligent Solutions seeks a senior Data Scientist and AI Architect to lead end-to-end data science and agentic AI initiatives in a production environment. You will design scalable analytics, build ML models, and implement GenAI-powered agents integrated with enterprise systems.

You will guide architecture decisions, mentor junior staff, and work closely with business stakeholders. This role emphasizes reliable, scalable AI solutions across cloud platforms and modern API services.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or related field.

Responsibilities

• Lead the application of Statistics, ML, forecasting and optimization to complex CPG/Retail problems.
• Design analytical approaches for large datasets.
• Develop, validate, and optimize ML and forecasting models.
• Identify modelling approaches based on business objectives and data characteristics.
• Establish robust model validation and performance measurement frameworks.
• Translate analytical findings into actionable business recommendations.
• Design and develop production-grade AI agents and multi-agent systems.
• Define agent architecture, orchestration, tool usage and workflow design.
• Build LLM-based analytical workflows that reason over data and interact with tools and business systems.
• Design RAG solutions and retrieval strategies for use cases.
• Develop approaches for agent memory, state management and workflow execution.
• Design evaluation frameworks to measure AI outputs quality and reliability.
• Address hallucination, reliability and failure modes in AI apps.
• Stay current with Agentic AI and GenAI technologies and assess practical use.

Skills

Data Science

Machine Learning

GenAI / LLMs

Python

SQL

Model Validation

Productionizing AI

API Development

PostgreSQL

Git

Docker

Cloud Platforms (AWS/Azure/GCP)

Agentic AI / Multi-Agent Systems

English Communication

Education

Bachelor's or Master's in CS/DS/Statistics/Mathematics/Engineering

Tools

FastAPI

Docker

Git

LangChain

LangGraph

LLM APIs

Vector Databases

Job description

Key Responsibilities

Data Science & Advanced Analytics

• Lead the application of Statistics, Machine Learning, forecasting, prediction and optimization techniques to complex CPG/ Retail business problems.


• Design analytical approaches for large and complex datasets.


• Develop, validate, and optimize machine learning and forecasting models.


• Identify appropriate modelling approaches based on business objectives and data characteristics.


• Establish robust model validation and performance measurement frameworks.


• Translate complex analytical findings into actionable business recommendations.

Agentic AI & Generative AI

• Design and develop production-grade AI agents and multi-agent systems.


• Define agent architecture, orchestration, tool usage and workflow design.


• Build LLM-based analytical workflows that can reason over business data and interact with tools, APIs and business systems.


• Design RAG solutions and retrieval strategies appropriate to the use case.


• Develop approaches for agent memory, state management and workflow execution.


• Design evaluation frameworks to measure accuracy, reliability, groundedness and quality of AI/agent outputs.


• Identify and address hallucination, reliability, and failure-mode issues in AI applications.


• Stay current with emerging Agentic AI and GenAI technologies and assess their practical application.

Solution Architecture & Engineering

• Design end-to-end AI solution architectures covering Data Science, LLMs, agents, APIs, databases and deployment.


• Develop backend services using Python, FastAPI or equivalent frameworks.


• Integrate databases, ML models, LLMs, APIs and external tools.


• Review code and technical implementations for quality, scalability, and maintainability.


• Establish engineering and development best practices for AI solutions.

Cloud & Production

• Lead containerization and deployment of AI applications using Docker.


• Deploy and operate AI services on AWS, Azure or GCP.


• Contribute to CI/CD, monitoring, logging and production reliability.


• Work with Platform/DevOps teams on scalable AI application architecture.


• Diagnose production issues and drive improvements in system reliability and performance.

Technical Leadership

• Own AI solutions from problem definition through production.


• Break complex problems into executable technical components.


• Mentor Data Scientists and junior team members.


• Review technical approaches and provide guidance on modelling and AI architecture.


• Collaborate with Data Engineering, Software Engineering and Platform/DevOps teams.


• Work directly with business stakeholders and clients when required.


• Contribute to technical standards, reusable components and AI best practices within the organization.

Skills & Qualifications REQUIRED SKILLS & QUALIFICATIONS

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or a related discipline.


• 5+ years of experience in Data Science, AI, Machine Learning, GenAI or related areas.


• Strong Statistics and Machine Learning fundamentals: forecasting, prediction, optimization, model validation, large-scale data analysis.


• Strong Python and SQL skills.


• Significant hands‑on experience with Generative AI / LLMs (RAG, embeddings, prompt engineering, LLM evaluation).


• Demonstrated experience designing and building AI agents or agentic workflows -including agent orchestration, tool calling, agent memory/state, and agent evaluation. Experience limited to prompt engineering, basic LLM API integrations or simple chatbot development is not sufficient.


• Experience taking AI/ML solutions into production.


• Experience developing APIs or backend services (FastAPI or similar).


• Working knowledge of PostgreSQL or an equivalent relational database, and Git/version control.


• Hands‑on experience with Docker and at least one cloud platform (AWS, Azure or GCP).


• Strong problem‑solving and analytical ability, with the ability to independently own technical problems and drive them to completion.

PREFERRED SKILLS

• LangGraph, LangChain or equivalent agent orchestration frameworks.


• Advanced RAG architectures and vector databases.


• Multi-agent systems design.


• Guardrails and reliability engineering for LLM/agent outputs.