Director

Director of AI Engineering

Antal InternationalVisit website ↗·Posted 1 month ago

Location

Bengaluru, Karnataka, India

Required Skills

AIDevOpsCI/CD

About the Role

Experience, Skills . Accomplishments

• Minimum 15. years of experience in software engineering, including 5. years in senior engineering leadership roles (Senior Director / Director).

• Proven experience leading leaders of leaders across complex, multi-product product engineering organizations.

• Strong technical background across the full software stack, including cloud-native, distributed, and data-intensive systems.

• Demonstrated experience delivering production-grade Generative AI and Agentic AI solutions, including:

• LLM-powered applications and services

• Agentic workflows and orchestration frameworks

• Model integration, evaluation, and lifecycle management

• MLOps / LLMOps practices

• Experience partnering with Data Science and AI Research teams to operationalize AI at scale.

• Prior experience working in a product-focused software company.

• Strong executive communication and stakeholder management skills

It would be great if you also had

• Experience building data and AI platforms using proprietary and third-party datasets in regulated environments.

• Background in Life Sciences . Healthcare or other highly data-intensive, regulated domains.

• Experience with responsible AI, data governance, and compliance frameworks.

Track record of driving
enterprise-scale software engineering or AI transformation initiatives

What You Will Be Doing

AI . Software Engineering Platform Leadership

• Lead engineering strategy and execution for data and software platforms aligned to AI-driven products across multiple Market Access solutions.

• Drive the design and delivery of AI-first architectures, including LLM-powered services, agentic workflows, orchestration layers, and human-in-the-loop systems.

• Build robust data and software foundations that enable advanced analytics, AI inference, and real-time decisioning at scale.

• Partner with Data Science, AI Research, and Architecture teams to operationalize models into reliable, compliant, and enterprise-grade production systems.

• Establish platform capabilities for prompt management, model evaluation, observability, governance, and responsible AI.

Organizational . Engineering Leadership

• Lead and scale multiple Director- and Senior Manager–led engineering organizations delivering both AI-enabled and core product capabilities.

• Set clear expectations for end-to-end ownership across full stack, data, and AI-enabled engineering teams.

• Balance rapid AI innovation with enterprise-grade standards for reliability, security, performance, and maintainability.

Technical . Platform Strategy

• Influence and define enterprise standards for software engineering excellence and AI-enabled development, including architecture, coding standards, testing, CI/CD, DevOps/SRE, MLOps, LLMOps, and agent lifecycle management.

• Ensure platforms are cloud-native, scalable, secure, and compliant with data privacy, regulatory, and governance requirements.

• Drive adoption of AI-assisted development tools to improve engineering productivity and quality.

Product . Business Partnership

• Act as a senior technology partner to Product and Business leaders across Market Access and LS.H portfolios.

• Translate complex business and customer problems into scalable data, software, and AI solutions with measurable commercial and customer impact.

• Guide prioritization decisions by balancing innovation, technical debt, feasibility, risk, cost, and time-to-market.

People, Culture . Talent

• Build, mentor, and retain a strong leadership bench across Directors and Senior Managers with expertise in product engineering, data platforms, and AI-enabled systems.

• Shape hiring strategies to attract senior Full Stack, Data, and AI Platform engineering talent.

• Foster a culture of engineering excellence, accountability, continuous learning, and responsible innovation.

Operational Excellence . Governance

• Establish metrics and governance across software, data, and AI platforms covering quality, reliability, cost, performance, security, and business impact.

• Reduce operational risk through disciplined engineering practices, observability, and continuous improvement.

• Partner with Security, Legal, Compliance, and Privacy teams to ensure responsible, ethical, and compliant AI deployment.

HireIQ AI InsightsBeta

Ideal Candidate

A seasoned engineering leader (15+ years) who has shipped production Generative AI systems at enterprise scale—not just piloted them—and has hands-on credibility across MLOps, cloud-native architectures, and regulated data environments.

Estimated Salary Range(medium confidence)

₹25 L – ₹45 L per year

Likely Interview Questions

1.Walk us through a Generative AI or Agentic AI system you moved from research to production. What were the top 3 operational challenges, and how did you solve the LLMOps/model lifecycle piece?
🔒 4 more questions locked — unlock with Professional + run a full mock interview

🔒 Strengths to highlight + red flags locked.

Land this role fasterProfessional

🎙️