Data Scientist

Ericsson; Kolkata District; On-site; INR 1,500,000 - 3,200,000; Full time

Job summary

Ericsson is seeking a passionate Data Scientist to design and deliver production-grade AI solutions including GenAI applications, AI Agents, andEnterprise Copilots for telecom OSS/BSS. You will work with LLMs/SLMs, Knowledge Graphs, and cloud-native AI platforms to solve complex problems at scale.

You will collaborate with architects, data engineers, and stakeholders to deploy AI solutions using AWS Bedrock or Vertex AI, while ensuring governance and reliable performance in a cloud-native

Qualifications

• Strong foundation in Statistics, ML, DL, NLP, and Generative AI.
• Expert Python programming for production-grade AI/GenAI apps.
• Hands-on experience with AI Agents, Multi-Agent Systems, and Graph RAG.
• Knowledge Management, Knowledge Graphs, Semantic Search, and enterprise retrieval architectures.
• Experience with MCP (Model Context Protocol)-based apps.
• Experience training/fine-tuning LLMs/SLMs.
• Expertise with AWS Bedrock and/or Vertex AI for enterprise deployment.
• Cloud-native development, APIs, containers, Kubernetes, and MLOps.

Responsibilities

• Design and develop production-grade GenAI applications and AI Agents.
• Build Enterprise Copilots using LLMs/SLMs, MCP, RAG, and Graph RAG.
• Develop Knowledge Management solutions leveraging Knowledge Graphs and vector databases.
• Train, fine-tune, and optimize LLMs/SLMs for domain use cases.
• Build scalable Python-based AI services and APIs.
• Deliver enterprise AI solutions using AWS Bedrock and/or Vertex AI.
• Collaborate with architects, data engineers, and business stakeholders.

Skills

Statistics

Machine Learning

Deep Learning

NLP

Generative AI

Python production-grade

AI Agents/GenAI apps

Knowledge Graphs

MLOps/Kubernetes

AWS Bedrock/Vertex AI

Tools

Kubernetes

Job description

About The Role

Ericsson is looking for a passionate Data Scientist to build next-generation AI solutions for Telecom OSS/BSS. You will design and deliver production-grade AI Agents, Enterprise Copilots, and intelligent automation using Generative AI, LLMs/SLMs, Knowledge Graphs, and cloud-native AI platforms to solve complex business problems at scale.

Key Responsibilities

• Design and develop production-grade GenAI applications and AI Agents.
• Build Enterprise Copilots using LLMs/SLMs, MCP, RAG, and Graph RAG.
• Develop Knowledge Management solutions leveraging Knowledge Graphs, vector databases, and enterprise data.
• Train, fine-tune, optimize, and evaluate LLMs/SLMs for domain-specific use cases.
• Build scalable Python-based AI services and APIs.
• Deliver enterprise AI solutions using AWS Bedrock and/or Google Vertex AI.
• Collaborate with architects, data engineers, and business stakeholders to deliver AI-driven business outcomes.

Required Skills

• Strong foundation in Statistics, Machine Learning, Deep Learning, NLP, and Generative AI.
• Expert Python programming with experience building production-grade AI/GenAI applications.
• Hands‑on experience with AI Agents, Multi‑Agent Systems, Enterprise Copilots, RAG, Graph RAG, embeddings, vector databases, and agent orchestration frameworks.
• Strong understanding of Knowledge Management, Knowledge Graphs, Semantic Search, and enterprise retrieval architectures.
• Experience developing MCP (Model Context Protocol)-based applications.
• Experience training, fine‑tuning, and optimizing LLMs/SLMs.
• Expertise in AWS Bedrock and/or Google Vertex AI, including AgentCore/Agents, Knowledge Bases, Guardrails, Prompt Management, Model Customization, Vertex AI Agent Builder, and enterprise AI deployment.
• Experience with cloud-native development, APIs, containers, Kubernetes, and MLOps.

Preferred Qualifications

• Telecom OSS/BSS domain knowledge.
• Experience with AI Reasoning, Agent Memory, Planning, and Tool Use.
• Knowledge of AI Observability, Explainability, Responsible AI, and Governance.
• Experience with Databricks, Snowflake, MLflow, or similar AI platforms.
• Master's/Ph.D. in Computer Science, AI, Data Science, or a related field is preferred.
• Publications, patents, or open-source contributions in AI/ML are an added advantage.