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Senior Applied AI Researcher (India)

Articul8· Applied Research
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Hybrid (India/Bengaluru)Full-timePosted 5mo ago

About the role

Senior AI researcher role focused on end-to-end research programs for a domain-specific GenAI platform, spanning model training, reinforcement learning, multimodal understanding, and knowledge representation with depth in at least one area.

Responsibilities

  • Own and orchestrate end-to-end research programs using massively parallel agentic AI — from problem formulation through production deployment, designing agent-driven experiment campaigns that explore architectures, training regimes, data strategies, and evaluation criteria
  • Go deep: drive breakthrough domain-specific model quality — lead multi-stage training pipelines, domain adaptation, RL-based optimization (RLHF, DPO, reward modeling), and training dynamics analysis with parallel ablations and sweeps
  • Go broad: span modalities, methods, and domains — design and train multimodal systems (text, images, tables, charts, technical documents), knowledge graph pipelines, hybrid retrieval architectures, and structured reasoning systems
  • Architect agentic data and training infrastructure — build agent-orchestrated pipelines for domain-specific data curation, quality filtering, preprocessing, and large-scale training
  • Mentor AI Researchers in the agentic paradigm — coach team members on agent workflows to amplify depth and breadth
  • Compress the research-to-production cycle — leverage agentic CI/CD, automated integration testing, and continuous evaluation with engineering/product/domain collaboration
  • Build force-multiplying knowledge systems — document findings, publish at top-tier venues, contribute to internal knowledge infrastructure
  • Model the augmented researcher — identify bottlenecks in workflows and design scalable solutions to maximize human potential

Qualifications

  • Education: PhD or MSc in Computer Science, Machine Learning, or related field
  • Experience: 5+ years as an AI/ML researcher with shipped research artifacts (models, systems, or tools in production), including 2+ years building LLM-based systems
  • Model training depth: experience with multi-stage training pipelines (pretraining, fine-tuning, post-training) and diagnosing training issues from loss curves, gradient norms, and evaluation metrics
  • Technical specialization: deep expertise in at least one of domain-specific model adaptation, multimodal learning, RLHF, knowledge-grounded generation, or retrieval-augmented systems with published or shipped production work
  • Distributed systems: hands-on experience with distributed training at scale (e.g., DeepSpeed, FSDP, Megatron-LM) and understanding data vs model parallelism
  • Software engineering: production-grade Python, clean abstractions, well-tested code, building tools others depend on

Skills

Required:Domain-specific model adaptationMultimodal learningReinforcement learning from human feedback
Nice to have:Knowledge-grounded generation

Benefits

  • [Not specified in the description]
Last updated Aug 5, 2026. Interested? Apply on the company site.
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