8 Data Infrastructure and AI Operation Engineer. Data Infrastructure, Ingestion & AI Operations Department: Data Engineering & Infrastructure Employment Type: Full time

Location:
Mumbai, India Reports to CEO

Experience:
2+ Years Salary : As per industry The Role We are seeking a hands-on Member of Technical Staff to own the operational and technical pipeline that turns data received from multiple sources into a centralized, organized, auditable and usable data repository. This is an execution-heavy infrastructure and data engineering position: you will work directly with terabytes to petabyte-scale data, storage hardware, cloud platforms, scripting, and AI-assisted workflows. You should be equally comfortable debugging a file-transfer failure at the command line, managing a NAS, and designing a repeatable ingestion and quality-control process.

Key Responsibilities

• Data collection and ingestion: Coordinate technical receipt of datasets from multiple suppliers in India and other markets, including disks, NAS shares, secure links, cloud buckets and hosted repositories. Build reliable bulk-upload, download and synchronization workflows.
• Centralization and organization: Consolidate incoming datasets into approved Google Drive / Google Workspace locations and/or cloud object storage (Google Cloud Storage, Amazon S3, Azure Blob Storage), using clear directory structures, naming conventions, metadata catalogs and source-level inventories.
• NAS and storage systems: Configure, operate and troubleshoot network-attached storage using SMB/CIFS, NFS, rsync, SFTP/SSH and related protocols. Understand RAID, storage pools, mounting, permissions, snapshots, disk-health monitoring, network throughput, backup and recovery.
• Cloud infrastructure: Work across GCP, AWS and Microsoft Azure, including compute instances, object storage, IAM, CLI tools, service accounts, lifecycle policies, transfer utilities, logging and cost controls. Select storage and transfer approaches based on volume, security, performance and budget.
• Low-level and systems work: Use Linux, shell scripting, Python and systems utilities to automate file handling, large transfers and data transformations. Diagnose filesystem, permissions, network, memory,



disk I/O and performance bottlenecks; write efficient, resilient scripts rather than relying solely on graphical tools.
• File-format fluency: Inspect, validate, convert, extract and organize heterogeneous data, including PDF, DOCX, TXT, CSV, JSON, XML, images, video, audio, archives, databases and domain-specific engineering/CAD formats such as DWG, DXF, STEP, STL, IFC and related formats when required.
• Cleaning and quality assurance: Identify duplicates, corrupted or incomplete files, inconsistent metadata, unusable naming, format issues and missing records. Implement checksum verification, deduplication, schema validation, basic parsing, batch transformation and exception reports.
• AI-enabled operations: Use Claude and OpenAI models/APIs and coding assistants to accelerate scripting, document classification, metadata extraction, categorization, normalization, format triage and QA. Design review steps and evaluate outputs; do not upload sensitive or restricted supplier data to external AI services without authorization.
• Traceability and governance: Maintain provenance records for each supplier and batch, including receipt date, original location, file count, size, format, transformations, access restrictions and delivery status. Apply least-privilege access, encryption and retention controls.
• Operational ownership: Monitor ingestion jobs, maintain runbooks and dashboards, resolve transfer failures, coordinate with vendors and internal stakeholders, and deliver organized, verified datasets against agreed timelines. Required Qualifications
• Practical experience in data engineering, infrastructure engineering, DevOps, systems administration or a similarly hands-on technical role, with evidence of working on large heterogeneous datasets.




• Strong Linux command-line skills and proficiency in Python and Bash/shell scripting; ability to read and modify lower-level utilities or code and troubleshoot systems behavior.
• Experience with at least one major cloud platform (GCP, AWS or Azure) and working familiarity with the others; experience with large object-storage transfers and cloud CLIs.
• Hands-on familiarity with NAS, shared filesystems, file-transfer protocols, storage permissions and backup practices.
• Comfort handling many file formats and building repeatable workflows for parsing, validation, conversion, cleaning and deduplication.
• Proficiency using Claude and OpenAI tools/models for technical work, together with sound judgment on accuracy, confidentiality and human review.
• High attention to detail, strong written documentation, a security-first approach, and ability to independently manage ambiguous operational tasks. Preferred Qualifications
• Experience with multi-terabyte or petabyte-scale repositories, rclone, rsync, AWS CLI, gcloud CLI, AzCopy, parallel transfers, resumable jobs and checksum-based integrity checks.
• Knowledge of Docker, Git, SQL, metadata/indexing systems, batch processing, OCR or document AI pipelines.
• Exposure to licensing-aware data operations, privacy-sensitive datasets, CAD/AEC files, multimedia corpora, or AI model training-data preparation.
• Experience with network performance optimization, observability, storage cost management and disaster recovery. What Success Looks Like
• Every incoming supplier batch has a documented inventory, provenance, validation status and clearly identified central storage location.
• Bulk ingestion and synchronization are automated, resumable and verified; failures are surfaced promptly and recoverable.
• Datasets are consistently named, deduplicated where appropriate, access-controlled and easy for authorized teams to locate and use.
• AI-assisted classification and cleanup improve throughput without compromising quality, confidentiality or traceability. Working style: This role requires hands-on execution, comfort with both physical and cloud storage, ownership from initial data receipt through usable delivery, and direct collaboration with the CEO and data partners. Mail at [email protected]

📌 Data Infrastructure and AI operation Engineer (Mumbai)
🏢 Talent Corner HR Services
📍 Mumbai