Job Summary

Role: Data Scientist / Data Engineer Domain Intelligence AI Engineering, WPM/EAMS Focus

Location: Offshore - India/ 100% Remote

Employment Type: 12+ Months Contract

Experience Required: 5+ years in Data Engineering, including Data Science, AI/ML. 1+ year(s) experience in WPM/EAMS or similar manufacturing and asset management platforms

Overview

We are looking for a hands-on Data Scientist / Data Engineer to support domain intelligence, AI-driven data initiatives, and skill development for WPM/EAMS and broader PGS Ops use cases. The role combines strong data engineering and data science capabilities with domain knowledge to transform operational workflows, data patterns, and SME expertise into high-quality data products, reusable AI skills, and intelligent automation solutions.

The ideal candidate will work closely with SMEs, business stakeholders, data scientists, and engineering teams to build scalable data pipelines, analyze complex datasets, develop AI/GenAI solutions, and validate agent-generated outputs for business accuracy and usability.

Key Responsibilities

- Collaborate with SMEs and business stakeholders to translate WPM/EAMS operational workflows and domain knowledge into structured, reusable AI skill artifacts.
- Design and build scalable data pipelines, ELT/ETL workflows, and datamodels using Snowflake and cloud platforms.
- Analyze, profile, and transform datasets to identify relationships, patterns, business rules, and transformation logic relevant to WPM/EAMS and PGS Ops domains.
- Develop and optimize datamodels using dimensional, datavault, or other appropriate modeling techniques.
- Prepare high-quality, trusted datasets to support AI/ML, GenAI, analytics, and emerging intelligent automation use cases.
- Develop solutions leveraging LLMs, prompt engineering, Snowflake Cortex, Snowpark ML, and other AI technologies.
- Support RAG-based applications, embeddings, vector search, knowledge retrieval,



and intelligent automation use cases.
- Integrate enterprise AI services such as Azure OpenAI or similar platforms with data and analytics ecosystems.
- Validate and refine AI- and agent-generated skills, insights, and outputs to ensure business correctness, accuracy, consistency, and usability.
- Define quality metrics, validation frameworks, and acceptance criteria for data products, AI skills, and intelligent solutions.
- Ensure data quality, reliability, governance, security, performance, and cost optimization across data and AI platforms.
- Support MLOps practices and the integration of AI/ML pipelines into enterprise data platforms.
- Collaborate with business, analytics, datascience, engineering, and domain teams to deliver end-to-end solutions.
- Contribute to architecture decisions, technical design, and continuous improvement of data and AI platforms.
- Mentor junior engineers and contribute to knowledge sharing and technical best practices.

Required Skills Experience

- 5+ years of experience in data engineering, data science, AI/ML, or a related field, with hands-on experience building enterprise data and AI solutions.
- Robust experience in data engineering, data science, data analysis, and applied AI/ML.
- Strong expertise in Snowflake, including advanced features, performance tuning, data sharing, governance, and cost optimization.
- Proficiency in SQL and Python.
- Experience designing and developing scalable ETL/ELT pipelines and data models.
- Strong understanding of dimensional modeling, DataVault, or similar datamodeling methodologies.




- Hands-on experience with LLMs, prompt engineering, and Generative AI.
- Experience with Snowflake Cortex, Snowpark ML, or similar AI/ML platforms.
- Knowledge of RAG architectures, embeddings, vector databases, vector search, and knowledge retrieval frameworks.
- Experience with MLOps concepts and AI/ML pipeline integration.
- Experience with cloud platforms such as Azure, AWS, or GCP; Azure experience is preferred.
- Prior experience with WPM, EAMS, manufacturing, asset management, or similar operational systems is preferred.
- Ability to translate functional and domain requirements into datalogic, analytical insights, and structured AI solutions.
- Strong problem-solving, analytical, communication, and stakeholder management skills.

Education

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Information Technology, or a related field.

Tools Technologies

Data Platforms

- Snowflake
- Databricks

ETL / ELT Orchestration

- dbt
- Azure DataFactory
- Apache Airflow

Programming Data Processing

- Python
- SQL
- Apache Spark
- Kafka / Azure Event Hubs

AI / ML GenAI

- Snowflake Cortex
- Snowpark ML
- Azure OpenAI or similar LLM platforms
- MLflow
- LLMs and Prompt Engineering
- RAG Frameworks
- Embeddings and Vector Databases
- Vector Search

Cloud Platforms

- Microsoft Azure
- AWS
- GCP

Preferred Domain Experience

- WPM/EAMS or similar manufacturing and asset management platforms.
- Manufacturing, life sciences, industrial operations, or enterprise asset management domains.
- Experience working with SMEs to capture business knowledge and convert it into Structured data, analytical, or AI artifacts.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Data Scientist / Data Engineer - Domain Intelligence & AI Engineering (India)
🏢 Ekloud
📍 India