Applied AI Research Engineer

Jobgether SRL; India; On-site; INR 400,000 - 700,000; Full time

Benefits offered by this job

Remote in India

Autonomy

Challenging AI problems

Collaborative teams

Curiosity-driven culture

Reusable AI assets

Remote flexibility

Job summary

Jobgether SRL in India seeks an Applied AI Research Engineer for a partner company. The role converts cutting-edge AI research into practical, reusable systems, including RL environments, LLM pipelines, and evaluation frameworks.

The position offers significant autonomy in a remote environment and a fast-moving research-to-production workflow. You will collaborate with AI research teams and cross-functional colleagues to turn ideas into production-ready assets, conduct experiments, and document

Qualifications

• Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Machine Learning, or a related technical discipline.
• At least 3 years of professional engineering or relevant industry experience in AI/ML, software engineering, or a closely related field.
• Hands-on experience developing agentic systems, reinforcement learning environments, LLM pipelines, or comparable AI applications.
• Proven experience creating evaluation harnesses, benchmarks, model-testing pipelines, or other systematic approaches to measuring AI system performance.
• Strong understanding of experimentation, reproducibility, evaluation methodologies, and technical documentation.

Responsibilities

• Build reinforcement learning and agent environments for real-world AI use cases, including defining task specifications, scoring mechanisms, evaluation criteria, and relevant testing workflows.
• Develop benchmarks and evaluation harnesses to assess model and data quality across dimensions such as accuracy, robustness, safety, latency, and cost.
• Design and implement LLM pipelines and agentic systems that support research initiatives, model evaluation, experimentation, and customer trials.
• Conduct fine-tuning, adapter, and other model experiments to understand how different datasets, techniques, and configurations influence model behavior and system performance.
• Deploy local and self-hosted models for evaluation, inference, experimentation, and automation workflows.
• Document experiments, configurations, datasets, results, methodologies, and known limitations clearly so that other engineers can reproduce, validate, and extend the work.
• Collaborate closely with AI research teams and cross-functional stakeholders to translate technical concepts into practical, reusable solutions and assets.
• Independently investigate technical problems, rapidly prototype potential approaches, and turn research questions or ideas into functional, production-oriented implementations.
• Contribute to the development of reliable, maintainable AI systems while applying strong engineering practices throughout experimentation and deployment.

Skills

AI research

Reinforcement learning

LLM pipelines

Evaluation frameworks

Experimentation

Documentation

Independent work

Education

Advanced degree in CS/ML

Tools

SWE-bench

Self-hosted models

Open-weight models

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Applied AI Research Engineer based in India.

This role focuses on turning cutting-edge AI research concepts into practical, reusable systems for real-world applications. You will build reinforcement learning environments, agentic systems, LLM pipelines, and evaluation frameworks that support advanced AI initiatives and customer use cases. The position combines hands-on engineering with experimentation, model evaluation, and applied research. You will investigate how different data, models, and techniques influence AI system behavior and performance. Working with research and cross-functional teams, you will transform technical ideas into reliable assets that can be reproduced and reused. The role offers significant autonomy in a remote environment and is ideal for an engineer who enjoys moving quickly from research questions to working solutions.

• Build reinforcement learning and agent environments for real-world AI use cases, including defining task specifications, scoring mechanisms, evaluation criteria, and relevant testing workflows.


• Develop benchmarks and evaluation harnesses to assess model and data quality across dimensions such as accuracy, robustness, safety, latency, and cost.


• Design and implement LLM pipelines and agentic systems that support research initiatives, model evaluation, experimentation, and customer trials.


• Conduct fine-tuning, adapter, and other model experiments to understand how different datasets, techniques, and configurations influence model behavior and system performance.


• Deploy local and self-hosted models for evaluation, inference, experimentation, and automation workflows.


• Document experiments, configurations, datasets, results, methodologies, and known limitations clearly so that other engineers can reproduce, validate, and extend the work.


• Collaborate closely with AI research teams and cross-functional stakeholders to translate technical concepts into practical, reusable solutions and assets.


• Independently investigate technical problems, rapidly prototype potential approaches, and turn research questions or ideas into functional, production-oriented implementations.


• Contribute to the development of reliable, maintainable AI systems while applying strong engineering practices throughout experimentation and deployment.

Requirements:

• Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Machine Learning, or a related technical discipline.


• At least 3 years of professional engineering or relevant industry experience in AI/ML, software engineering, or a closely related field.


• Strong software engineering capabilities with demonstrated experience building reliable, maintainable, and reusable AI or software systems.


• Hands‑on experience developing agentic systems, reinforcement learning environments, LLM pipelines, or comparable AI applications.


• Proven experience creating evaluation harnesses, benchmarks, model‑testing pipelines, or other systematic approaches to measuring AI system performance.


• Strong understanding of experimentation, reproducibility, evaluation methodologies, and technical documentation.


• Ability to work independently on complex technical problems, exercise sound engineering judgment, and move efficiently from an idea or research question to a working solution.


• Strong analytical and problem‑solving skills, with curiosity and enthusiasm for experimenting with emerging AI techniques and technologies.


• Experience with synthetic data generation systems or dataset development is a plus.


• Published research papers, benchmarks, or other technical research is advantageous.


• Experience with SWE‑bench or comparable software engineering evaluation environments is desirable.


• Experience building or deploying local inference systems, open‑weight models, or self‑hosted model environments is a plus.

Benefits:

• Permanent, regular full‑time position with a remote working arrangement in India.


• High degree of autonomy and ownership over applied AI research and engineering projects.


• Opportunity to work on advanced AI challenges spanning reinforcement learning, agentic systems, LLMs, model evaluation, and AI experimentation.


• Exposure to practical research applications supporting both advanced AI initiatives and real‑world customer use cases.


• Opportunity to collaborate closely with AI research and cross‑functional technical teams.


• Environment that values curiosity, accountability, innovation, collaboration, and continuous learning.


• Opportunity to develop reusable AI assets, evaluation frameworks, and systems that can influence future AI applications.


• Flexibility to work effectively in a remote environment while accessing tools, resources, and development opportunities to strengthen technical capabilities.