Forward Deployed Software Engineer

Vibehackers; India; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Vibehackers seeks a Forward Deployed AI Engineer to turn LLM and generative AI prototypes into production-grade automation within engineering and system test environments. You will work hands-on with system test engineers, automation teams, QA, and software developers to integrate AI solutions into existing automation frameworks, APIs, and workflows.

The role focuses on deploying, monitoring, troubleshooting, and iterating AI-powered automation to reduce manual testing effort and improve system

Qualifications

• 2+ years of hands-on software engineering, applied AI, or a related field.
• Experience deploying LLM / Generative AI applications beyond prototype into real-world or production environments.
• Experience integrating AI solutions with enterprise applications, automation frameworks, APIs, or other software systems.
• Demonstrated experience taking an AI or automation solution from prototype to real-world usage.
• Experience troubleshooting and improving AI behavior, including prompt/instruction tuning, output validation, guardrails, failure analysis, and diagnosing low-quality outputs.
• Strong software engineering, debugging, and problem-solving skills.
• Experience working closely with engineers or end users and iterating solutions based on live feedback.

Responsibilities

• Convert LLM/Generative AI prototypes into production-ready, repeatable solutions.
• Integrate AI solutions with engineering applications, automation frameworks, APIs, and workflows.
• Build orchestration and integration logic across multiple systems using Python.
• Deploy, monitor, troubleshoot, and continuously improve AI-powered solutions in live engineering environments.
• Work with system test and automation teams to understand workflows, constraints, failure modes, and identify safe AI opportunities to reduce manual effort.
• Develop AI-powered workflows that assist with generating structured test assets and adapt to test data, schemas, and configurations.
• Implement and customize agentic AI workflows for engineering and test automation use cases; tune prompts and AI behavior based on outcomes.
• Build validation, guardrails, and failure analysis to improve accuracy, reliability, and safety of AI outputs.
• Collaborate closely with engineers and end users, rapidly iterate on solutions, and debug complex software and AI issues across systems.

Skills

Software Engineering

Python

Development

Debugging

Problem Solving

Collaboration

Communication

AI Deployment

Integration

Monitoring

Troubleshooting

Prompt Tuning

Output Validation

Guardrails

Rapid Iteration

Requirement Analysis

Job description

Uses LLMs and agentic AI to productionize prototypes and automate engineering workflows; focused on deploying AI into real-world test automation.

About the Role

Work with engineering, QA, and automation teams to turn LLM and generative AI prototypes into production-ready, reliable solutions that integrate with existing test automation, APIs, and engineering workflows. The role focuses on deploying, monitoring, troubleshooting, and iterating AI-powered automation to reduce manual testing effort and improve system test quality.

Job Description

Role

Forward Deployed AI Engineer focused on taking LLM and generative AI prototypes into production within engineering and system test environments. You will work hands-on with system test engineers, automation teams, QA, and software developers to integrate AI solutions into existing automation frameworks, APIs, and workflows.

Key Responsibilities

• Convert LLM/Generative AI prototypes into production-ready, repeatable solutions.
• Integrate AI solutions with engineering applications, automation frameworks, APIs, and workflows.
• Build orchestration and integration logic across multiple systems using Python.
• Deploy, monitor, troubleshoot, and continuously improve AI-powered solutions in live engineering environments.
• Work with system test and automation teams to understand workflows, constraints, failure modes, and identify safe AI opportunities to reduce manual effort.
• Develop AI-powered workflows that assist with generating structured test assets and adapt to test data, schemas, and configurations.
• Implement and customize agentic AI workflows for engineering and test automation use cases; tune prompts and AI behavior based on outcomes.
• Build validation, guardrails, and failure analysis to improve accuracy, reliability, and safety of AI outputs.
• Collaborate closely with engineers and end users, rapidly iterate on solutions, and debug complex software and AI issues across systems.

Requirements

• 2+ years of hands-on experience in software engineering, applied AI, or a related field.
• Experience deploying LLM / Generative AI applications beyond prototype into real-world or production environments.
• Experience integrating AI solutions with enterprise applications, automation frameworks, APIs, or other software systems.
• Demonstrated experience taking an AI or automation solution from prototype to real-world usage.
• Experience troubleshooting and improving AI behavior, including prompt/instruction tuning, output validation, guardrails, failure analysis, and diagnosing low-quality outputs.
• Strong software engineering, debugging, and problem-solving skills.
• Experience working closely with engineers or end users and iterating solutions based on live feedback.
• Experience with REST APIs, Git, CI/CD, and at least one major cloud platform (AWS, Azure, or GCP).

Nice to Have

• Networking experience: TCP/IP, MPLS, gNMI, YANG
• Experience with test automation frameworks and system/network testing environments

Skills

Software Engineering Python Development Debugging Problem Solving Collaboration Communication AI Deployment Integration Monitoring Troubleshooting Prompt Tuning Output Validation Guardrails Rapid Iteration Requirement Analysis