𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀
𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟵𝟬𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟭𝟴𝟬𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟵𝟬-𝟭𝟴𝟬 𝗟𝗣𝗔)
Experience: 5+ yrs
Location: Bengaluru, Karnataka, India
Job Type: Full-time
We are looking for an experienced AI Research & Engineering Professional to contribute to the development and improvement of next-generation AI systems in collaboration with leading frontier AI research organisations.
The role focuses on building the infrastructure, environments, evaluations, and high-quality enterprise data required to improve advanced AI models. You will work at the intersection of AI research, software engineering, reinforcement learning, agentic systems, model evaluation, and data to develop solutions that help measure and enhance the capabilities of increasingly sophisticated AI systems.
Requirements
Key Responsibilities
- Design and develop reinforcement learning environments for coding, reasoning, and agentic AI tasks.
- Build infrastructure and tooling that enables advanced AI models to interact with realistic environments and complete complex tasks.
- Develop robust evaluation frameworks, benchmarks, and testing systems to measure model capabilities and behaviour.
- Design evaluation datasets, task suites, scoring mechanisms, and automated assessment pipelines.
- Work with coding agents and agentic systems to evaluate planning, execution, reasoning, and task completion.
- Develop scalable data pipelines and workflows for collecting, processing, validating, and managing enterprise AI data.
- Improve data quality, consistency, coverage, and usability for model training and evaluation.
- Analyse model outputs and evaluation results to identify capability gaps, failure patterns, and opportunities for improvement.
- Build tools that enable reproducible experimentation and reliable comparison of model performance.
- Collaborate with AI researchers and engineers to translate research requirements into production-quality systems.
- Design and implement automated testing, monitoring, and quality-control mechanisms for AI workflows.
- Investigate challenging technical problems involving model behaviour, evaluation reliability, data quality, and agent performance.
- Contribute to experimentation involving LLMs, reinforcement learning, AI agents, and model evaluation.
- Develop internal tools, frameworks, and reusable infrastructure to accelerate AI research and engineering workflows.
- Document technical approaches, experimental results, evaluation methodologies, and system designs.
- Continuously improve the reliability, scalability, and efficiency of AI research and production infrastructure.
- Stay current with developments in frontier AI, agentic systems, reinforcement learning, evaluation methodologies, and AI engineering.
What Makes You a Great Fit
- 5+ years of professional experience in AI/ML engineering, software engineering, machine learning research, data engineering, or a closely related technical discipline.
- Strong software engineering fundamentals with experience building production-grade, scalable systems.
- Strong programming skills in Python and proficiency with modern software development practices.
- Experience working with LLMs, generative AI, AI agents, reinforcement learning, or model evaluation.
- Practical experience designing evaluation frameworks, benchmarks, datasets, or automated testing systems for AI/ML models.
- Understanding of reinforcement learning environments, agentic workflows, or interactive AI systems is highly valuable.
- Strong analytical and problem-solving skills with the ability to investigate complex model and system behaviour.
- Experience working with structured and unstructured data and building reliable data-processing workflows.
- Strong understanding of experimentation, reproducibility, metrics, benchmarking, and statistical evaluation.
- Ability to translate ambiguous research problems into well-defined engineering solutions.
- Experience building APIs, services, infrastructure, developer tooling, or distributed systems is an advantage.
- Familiarity with cloud platforms, containers, CI/CD, databases, and modern engineering infrastructure.
- Strong attention to accuracy, reliability, data quality, and reproducibility.
- Ability to collaborate effectively with researchers, engineers, data specialists, and other technical stakeholders.
- Comfortable working in a fast-moving environment where requirements evolve alongside emerging AI capabilities.
- Strong curiosity about frontier AI and the development of increasingly capable and reliable AI systems.
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Engineering, or a related technical discipline is preferred.