Senior Applied AI Researcher (India)

About the role

Articul8 was born from a simple belief: GenAI should work for the enterprise, not the other way around. Our platform - combining domain-specific models, autonomous agentic reasoning (ModelMesh™), reliable model evaluation (LLM-IQ™), and multimodal understanding - serves regulated industries such energy, semiconductor, finance, aerospace, supply chain, and more. Trusted by Fortune 500 enterprises, we bring together research, engineering, product, and domain expertise to deliver AI that meets the accuracy, explainability, and auditability standards that high-stakes environments demand. Job Description: Articul8 AI is seeking a Senior Applied AI Researcher to solve open research problems across our domain-specific GenAI platform. You will own research projects end-to-end - from problem formulation through production deployment. This role spans model training, reinforcement learning, multimodal understanding, and knowledge representation - with deep expertise in at least one area.

Responsibilities

• Architect agentic data and training infrastructure - build agent-orchestrated pipelines for domain-specific data curation, quality filtering, preprocessing, and large-scale training that the entire research team can leverage to go faster
• Mentor AI Researchers in the agentic paradigm - coach team members on how to amplify their own depth and breadth by designing effective agent workflows, raising the ceiling on what every researcher can achieve
• Compress the research-to-production cycle - take prototypes to production-ready systems rapidly by leveraging agentic CI/CD, automated integration testing, and continuous evaluation harnesses, collaborating closely with engineering, product, and domain experts
• Build force-multiplying knowledge systems - document findings, publish at top-tier venues, and contribute to internal knowledge infrastructure that agentic tools can index and reason over, turning every breakthrough into compounding team-wide leverage
• Model the augmented researcher - continuously identify bottlenecks in your own and the team's workflows, then design or adopt efficient, scalable solutions that eliminate them - treating the maximization of human potential as a first-class research output
• Required Qualifications:
• Education: PhD or MSc in Computer Science, Machine Learning, or a 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: You have run multi-stage training pipelines (pretraining, fine-tuning, post-training) and can diagnose training failures from loss curves, gradient norms, and evaluation metrics - not just restart the job.
• Technical specialization: Deep expertise in at least one of: domain-specific model adaptation, multimodal learning, reinforcement learning from human feedback, knowledge-grounded generation, or retrieval-augmented systems. You've published or shipped production work in your area.
• Distributed systems: Hands-on experience with distributed training at scale (DeepSpeed, FSDP, Megatron-LM, or equivalent). You understand data parallelism vs. model parallelism and know when each matters.
• Software engineering: Production-grade Python, clean abstractions, tested code. You build tools others depend on.

Requirements

• Experience adapting models to specialized domains where standard benchmarks don't apply - you've had to define what "correct" means and build evaluation around it.
• Track record of taking a research prototype to a production system serving real users.
• Experience with knowledge graph construction, hybrid retrieval architectures, or structured reasoning systems in practice - not just in papers.
• Strong publication record with evidence of depth, not just breadth.
• Cloud-native ML infrastructure experience (Kubernetes, distributed job sch

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