Director, Applied AI- CTO

ANSR MedTech Capability Center; Bengaluru; On-site; INR 2,500,000 - 5,000,000; Full time

Job summary

ANSR MedTech Capability Center in Bengaluru seeks a Director, Applied AI to own the AI solution delivery capability for the CTO-aligned Insights & Analytics organization. You’ll identify AI opportunities, translate business problems into AI product requirements, and lead cross-functional delivery from concept through production deployment and value realization.

Lead a team across Data Science, Platform AI Engineering, Data Engineering, Analytics Engineering, Security, and Data Governance to

Job description

Job Summary

The Director, Applied AI owns the Applied AI solution delivery capability for the CTO-aligned Insights & Analytics organization. This leader identifies and shapes AI opportunities, translates business problems into AI product requirements, and leads cross-functional delivery from concept through production deployment and value realization. The role is accountable for AI products, copilots, agents, decision engines, workflow automation, Responsible AI governance, and adoption outcomes. The Director manages and partners closely with Data Science, Platform AI Engineering, Data Engineering, Analytics Engineering, Security, and Data Governance teams to deliver enterprise-scale AI solutions.

Key Responsibilities

AI Portfolio & Value Realization

Build and maintain a value-ranked Applied AI portfolio aligned to I&A priorities. Partner with I&A Commercial team to define business outcome metrics and manage AI demand pipeline, prioritization, and executive reviews.

AI solution design and delivery

Frame AI-ready problem statements, success metrics, user journeys, data readiness, change impacts, and operating-model implications. Establish intake, triage, prioritization, and stage-gate practices for AI use cases, including proof-of-value, pilot, production, scale, and retirement decisions. Lead design and delivery of applied AI products including copilots, agents, predictive decision engines, recommendation systems, workflow automation, and generative AI experiences. Define solution patterns for retrieval-augmented generation, prompt orchestration, tool use, human-in-the-loop workflows, model selection, evaluation, and integration with business applications. Convert data science and research outputs into usable AI products with clear product requirements, acceptance criteria, UX/workflow design, operational handoffs, and adoption plans.

AI Product Management

Define product vision, roadmap, success measures, and release strategy for AI-enabled capabilities. Prioritize product backlog based on business impact and user feedback. Ensure adoption and business process integration.

Data Science

Design, build, and deploy predictive models, customer segmentation frameworks, propensity scoring, churn analysis, and statistical models that drive business decision-making. Develop advanced analytical capabilities including survival analysis, time-series forecasting, causal inference, and optimization models. Implement model performance monitoring, drift detection, and retraining pipelines to ensure production models maintain accuracy over time.

LLMOps, MLOps, and production readiness

Partner with Platform AI Engineering to operationalize model, prompt, agent, and retrieval pipelines with CI/CD, evaluation automation, deployment gates, observability, rollback, and incident response. Define production readiness standards for latency, reliability, scalability, cost, data quality dependencies, model performance, drift, prompt/version control, and service-level expectations. Ensure every deployed AI solution has monitoring, feedback loops, outcome measurement, ownership, support model, and a continuous-improvement roadmap.

Responsible AI, risk, and compliance

Embed Responsible AI by design across the lifecycle, including use-case risk tiering, privacy and security review, fairness and bias considerations, explainability, human oversight, content safety, and regulatory alignment. Create required AI governance artifacts such as AI impact assessments, model cards, prompt/system documentation, evaluation reports, approval records, audit trails, and release readiness checklists. Implement continuous controls for AI quality and safety, including test suites, red-teaming, hallucination and toxicity checks, data leakage checks, monitorable guardrails, escalation paths, and periodic risk reviews.

AI Data Readiness & Quality

Implement Data quality requirements for AI solutions, Dataset certification for AI use, Readiness assessments for AI deployment. Enable data product certification framework: the quality, documentation, and reliability standards every analytical deliverable must meet before production release.

Leadership & organization building

Build and lead a high-performing applied AI team across applied AI product management, AI solution architecture, prompt/agent engineering, ML/LLM engineering, evaluation engineering, and AI delivery leadership. Establish communities of practice, reusable design patterns, delivery playbooks, and quality standards that accelerate responsible AI delivery across the organization. Coach teams to balance speed, experimentation, quality, safety, and enterprise-grade sustainability. Foster a delivery culture centered on quality, accountability, and continuous improvement consistent with how ANSR MedTechs established Data & AI teams operate globally.

Delivery methodology & technical standards

Define and enforce a unified delivery methodology across all squads consistent with how ANSR MedTechs established Data