𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀

𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟱𝟬𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟮𝟬𝟬𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟱𝟬-𝟮𝟬𝟬 𝗟𝗣𝗔)

Experience: 8+ yrs

Location: India

Job Type: Full-time

We are looking for an experienced and technically strong AI Research & Engineering Leader to work on advanced artificial intelligence initiatives focused on improving the capabilities, reliability, and evaluation of frontier AI models.

The role sits at the intersection of AI research, machine learning engineering, model evaluation, reinforcement learning, agentic systems, coding environments, and enterprise data. You will contribute to building sophisticated environments, evaluation systems, and high-quality data solutions that help advance the capabilities of next-generation AI models.

The ideal candidate combines strong technical depth with the ability to work on ambiguous, research-oriented problems and translate emerging AI concepts into robust, scalable systems.

Requirements

Key Responsibilities

• Lead the design and development of advanced AI research and engineering systems for frontier AI models.
• Build sophisticated reinforcement learning environments for coding, reasoning, tool use, and agentic workflows.
• Design environments that allow AI agents to interact with realistic software systems, tools, APIs, and external resources.
• Develop robust model evaluation frameworks, benchmarks, and test environments to measure AI capabilities and reliability.
• Create automated evaluation pipelines for LLMs, AI agents, coding systems, and reasoning workflows.
• Investigate model behaviour, identify capability gaps, and design experiments to improve performance.
• Develop and manage high-quality datasets, including enterprise and synthetic data, for AI training and evaluation.
• Build scalable infrastructure for experimentation, evaluation, data processing, and AI workflows.
• Develop systems for automated testing, benchmarking, regression detection, and continuous model evaluation.
• Work on agentic AI architectures, including planning, tool calling, multi-step reasoning, memory, and environment interaction.
• Design and execute controlled experiments to test new AI methodologies and approaches.
• Analyse experimental results and translate findings into actionable technical and research decisions.
• Collaborate closely with AI researchers, ML engineers, software engineers, and data specialists.
• Establish scalable, reproducible, and observable engineering practices for AI experimentation.
• Evaluate emerging AI technologies and research techniques and identify opportunities for practical application.
• Drive technical architecture, system design, implementation, and productionisation of successful research initiatives.
• Mentor engineers and researchers while establishing strong technical and engineering practices.
• Document methodologies, experiments, evaluation results, system architectures, and technical learnings.

What Makes You a Great Fit

• 8+ years of experience in AI/ML engineering, research engineering, machine learning, software engineering, or a closely related technical field.
• Deep understanding of Generative AI, Large Language Models, reinforcement learning, and modern AI systems.
• Proven experience building sophisticated AI/ML systems, research infrastructure, or production-grade machine learning platforms.
• Strong programming expertise in Python and modern AI/ML frameworks.
• Strong experience with LLM evaluation, benchmarking, experimentation, and model behaviour analysis.
• Experience designing evaluation frameworks, task environments, automated benchmarks, or model assessment systems.
• Practical experience with agentic AI, AI agents, coding agents, tool use, or multi-step reasoning systems.
• Strong understanding of reinforcement learning concepts, environments, rewards, training loops, or RL-based optimisation.
• Experience working with large-scale, synthetic, enterprise, or specialised datasets for AI applications.
• Strong software engineering fundamentals across system design, APIs, distributed systems, testing, observability, and production reliability.
• Experience working with cloud infrastructure, distributed computing, GPUs, or large-scale ML platforms is an advantage.
• Strong research mindset with the ability to formulate hypotheses, design experiments, analyse results, and iterate rapidly.
• Excellent problem-solving and debugging skills with the ability to tackle highly ambiguous technical challenges.
• Strong communication and collaboration skills when working with research and engineering teams.
• Demonstrated ability to take ownership of complex initiatives from concept and experimentation through implementation and evaluation.
• Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, Mathematics, or a related technical field is advantageous, though exceptional equivalent industry experience may also be considered.