ML Data Engineer

1 week ago

Mumbai, Maharashtra, India Applied Computing Technologies Ltd Full time US$ 90,000 - US$ 1,20,000 per year

Based remotely in India we are seeking a highly skilled ML data engineer to operate as part of our outsourced pilot deployment team.

The ideal candidate will have a deep background in data engineering (and the challenges of it in industrial environments). We require sectoral experience of AI/Software and ideally Oil & Gas / the energy sector or equivalent industrial environments. You will be responsible for managing the data engineering aspects of our pilot product installations in customer locations in India.

Experience, skills or attributes?

Sector Experience in Startups Oil & Gas / Industry:

  • Minimum 7 years of experience in fast-growing startup / scaleup environments (Seed to Series B) and OR industrial / oil & gas industries, demonstrating an ability to navigate both high-growth and regulated environments.

Customer-Focused Deployment Experience:

  • Experience of on premises programmes such as installing pilot programs directly in customer environments.
  • Ability to coordinate with technical counterparts to understand their needs, address challenges, and ensure a smooth implementation process.

Problem Solving & Adaptability:

  • Strong problem-solving skills and flexibility to adapt quickly in dynamic environments, with a proactive approach to resolving technical roadblocks.

Excellent Communication Skills:

  • Strong verbal and written communication skills for clear, effective updates with both technical teams and non-technical stakeholders.

Technical Proficiency & Key Tasks:

Client Data Ingestion

  • Collaborate with client-facing teams to ingest raw data in various formats (CSV, JSON, XML, log files, Excel, etc.).
  • Parse and interpret metadata, schema documentation, or ad hoc data notes from clients.

Data Cleaning & Transformation

  • Clean, standardize, and format time series data (e.g. sensor streams, usage logs, transactions).
  • Extract structured information from unstructured text (e.g. log messages, reports, or notes) using basic NLP techniques.
  • Perform deduplication, imputation, outlier handling, and timestamp normalization.

Preprocessing for AI/ML Pipelines

  • Convert datasets into forms required by researchers (e.g. feature matrices, windowed sequences).
  • Engineer basic statistical features (e.g. lagged variables, aggregates, trend indicators).
  • Package clean datasets with associated metadata, schemas, and documentation for downstream teams.

Documentation & Communication

  • Maintain detailed data preparation logs and reproducible scripts.
  • Communicate ambiguities or anomalies in data clearly to researchers and client-facing staff.
  • Experience working with Python (Pandas, NumPy, etc.) for data manipulation.
  • Familiarity with time series data structures and basic preprocessing (resampling, interpolation).
  • Comfort with CLI tools, file encodings, and large dataset handling.
  • Understanding of data types, data integrity, and common issues with real-world data. (edited) 

Success metric(s).

Data Integration:

  • Drive the progress and completion of key data engineering milestones such as data audit and integration & connection milestones as part of pilot installations.

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