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Artificial Intelligence Engineer
GoodSpace AI
Mumbai, IND
Full-time
Associate
Onsite
Discovered 1 weeks ago
LLMs in production deploymentsPrompt engineeringContext engineeringRetrieval-augmented generationAI agentsLLM evaluation
Free
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LLMs in production deploymentsPrompt engineeringContext engineering
Role Overview
The Forward Deployed Engineer role connects AI tools and operational processes by working closely with rig clerks, contracts executives, and other functions.
The role focuses on practical workflow improvement across drilling reports, NPT analysis, tender review, maintenance history, inspections, crew certification, spares, and mobilisation.
The engineer is expected to identify when a process must be fixed before an AI solution is introduced.
Key Skills for This Role
LLMs in production deploymentsPrompt engineeringContext engineeringRetrieval-augmented generationAI agentsLLM evaluation
Full Job Posting
About the Role
The Forward Deployed Engineer role connects AI tools and operational processes by working closely with rig clerks, contracts executives, and other functions.
The role focuses on practical workflow improvement across drilling reports, NPT analysis, tender review, maintenance history, inspections, crew certification, spares, and mobilisation.
The engineer is expected to identify when a process must be fixed before an AI solution is introduced.
Key Responsibilities
- Embed with one function at a time, shadowing work, mapping real workflows, quantifying hours, and selecting use cases based on value, frequency, and feasibility.
- Train users on their own work in prompting, context, verification, failure modes, and practical AI adoption.
- Create function champions, playbooks, prompt libraries, and SOPs in English and Hindi.
- Build assistants, extraction, drafting, summarization, internal search, and automation workflows that eliminate re-keying.
- Integrate solutions with ERP, maintenance, email, and shared-drive systems.
- Govern data access, approvals, retention, audit trails, costs, and human review, and own the AI policy.
- Measure hours, cycle time, rework, and cost; report failures, retire unused solutions, and maintain a ranked roadmap.
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