Responsible Ai

**Skill**:Responsible AI & Research Integration**

**Exp: 10 to 18 years**

**Location: Bangalore**

**Role Overview**

The Senior Scientist - Responsible AI & Research Integration is a hybrid role embedded across the Responsible AI Office and the AI Research Lab. This role is focused on advancing the frontiers of Responsible AI and AI safety while ensuring that research outcomes directly inform the company's products, platforms, and internal practices.

This role is approximately 60% focused on research—pursuing foundational and applied investigations into agentic safety, oversight mechanisms, system interoperability, evaluation methodologies, and the broader challenges of responsible and scalable AI. The remaining 40% is dedicated to translating these insights into tools, features, and governance components that can be embedded into internal systems and external offerings.

In addition to contributing original research and prototyping new approaches, the Senior Scientist plays an active role in building partnerships with academic labs, participating in external working groups that are shaping the field of Responsible AI, and providing strategic intelligence on the evolving ecosystem of responsible AI technologies and companies.

**Key Responsibilities**

**Research & Development (60%)**
- Lead and contribute to applied research efforts in Responsible AI, including topics such as model alignment, transparency, behavioral safety, agentic behavior, and oversight mechanisms for autonomous or multi-agent systems
- Translate research findings into components, evaluation methods, or controls that can be incorporated into product features, platform architecture, or internal governance frameworks
- Participate in the design and validation of tools that address emerging safety risks and implementation gaps in AI development and deployment

**Product Strategy & Market Intelligence (25%)**
- Develop product roadmap specifications by translating emerging research and market needs into concrete technical requirements
- Conduct technical due diligence and evaluation of early-stage startups in the AI safety, governance, and trust space
- Assess commercial viability and enterprise adoption potential of safety and governance solutions
- Monitor competitive landscape and identify market gaps in responsible AI tooling
- Act as a key connector between research and engineering teams, working closely with product leads and framework architects to ensure feasibility and alignment between scientific exploration and development priorities

**Ecosystem Development & Partnerships (15%)**
- Build and manage collaborative relationships with academic labs, research consortia, and external fellows, identifying opportunities for co-authored research, joint development of tools or benchmarks, and knowledge exchange
- Establish and maintain relationships with leading AI safety research groups globally (Stanford HAI, UC Berkeley CHAI, MIT CSAIL, Oxford, etc.)
- Scout and evaluate emerging companies developing solutions for AI oversight, interpretability, alignment, and governance
- Build relationships with AI safety startups and scale-ups to identify partnership, investment, or acquisition opportunities
- Participate in venture capital networks and startup accelerators focused on AI safety technologies

**External Representation & Thought Leadership**
- Coordinate and contribute to external working groups focused on advancing standards, best practices, or evaluation methodologies for Responsible AI and safety-aligned systems
- Represent the company in research summits, public forums, and academic communities to share work, shape dialogue, and help position the organization as a trusted leader in the development of responsible, high-performing AI systems
- Contribute to internal education and knowledge dissemination efforts by sharing research findings, facilitating workshops, and advising teams on complex or emergent risks in AI systems

**Required Qualifications**

**Technical Requirements**
- PhD in Computer Science, Artificial Intelligence, Cognitive Systems, or a related discipline
- Strong publication record or equivalent contributions in AI safety, agent alignment, multi-agent systems, fairness, interpretability, or risk-aware AI
- Proven ability to translate research into production-ready tools, software components, or product capabilities
- Hands-on experience conducting applied research and collaborating with engineering or product teams
- Familiarity with foundational model architectures, ML evaluation pipelines, and lifecycle governance frameworks
- Experience with agentic AI systems, multi-agent coordination, and autonomous system oversight
- Knowledge of AI governance frameworks, regulatory landscapes (EU AI Act, emerging US standards), and compliance requirements
- Understanding of cybersecurity implications for AI systems, especially autonomous agents

**Industry & Ecosystem Experience**
- Proven track record of evaluating ea