Sr Director, AI & Engineering (Hyderabad)

Sr Director, AI & Engineering (Hyderabad) Syneos Health® is a leading fully integrated biopharmaceutical solutions organization built to accelerate customer success. We translate unique clinical, medical affairs and commercial insights into outcomes to address modern market realities. Every day we perform better because of how we work together, as one team, each the best at what we do. We bring a wide range of talented experts together across a wide range of business-critical services that support our business. Every role within Corporate is vital to furthering our vision of Shortening the Distance from Lab to Life®. Discover what our 29,000 employees, across 110 countries already know. WORK HERE MATTERS EVERYWHERE Why Syneos Health We are passionate about developing our people, through career development and progression; supportive and engaged line management; technical and therapeutic area training; peer recognition and total rewards program. We are committed to our Total Self culture – where you can authentically be yourself. Our Total Self culture is what unites us globally, and we are dedicated to taking care of our people. We are continuously building the company we all want to work for and our customers want to work with. Why? Because when we bring together diversity of thoughts, backgrounds, cultures, and perspectives – we’re able to create a place where everyone feels like they belong. Job Responsibilities Sets the enterprise engineering and AI-enabled development-lifecycle strategy for a portfolio of products and platforms, translating business priorities into a multi-year capability roadmap, investment thesis, target architecture, and outcome framework. Leads Directors, engineering managers, principal engineers, and cross-functional leaders; defines organizational design, decision rights, workforce strategy, succession depth, and leadership expectations across a broad span of control. Owns the transformation from AI-assisted development to an AI-native operating model spanning discovery, specification, architecture, coding, review, testing, release, operations, knowledge management, and continuous improvement. Establishes enterprise guardrails for AI use in engineering, including approved tools and models, data and intellectual-property protection, human accountability, traceability, evaluation, access controls, model and prompt supply-chain risk, agent permissions, auditability, monitoring, rollback, and retirement. Sponsors common developer platforms, reusable AI and agent capabilities, context and knowledge services, reference architectures, paved-road workflows, and automated policy controls that enable teams to scale safely without duplicating solutions. Creates portfolio governance for use-case selection and progression from experiment to production, applying value, risk, feasibility, architecture, data readiness, regulatory impact, and total-cost criteria to build, buy, partner, scale, or retire decisions. Defines quality and safety standards for AI-generated and AI-enabled software, including specification quality, automated verification, secure development, adversarial and misuse testing, reliability engineering, observability, production evaluation, and incident learning. Ensures increased engineering throughput does not compromise stability or maintainability by strengthening internal platforms, loosely coupled architectures, automated testing, version-control discipline, fast feedback loops, and value-stream management. Owns a balanced portfolio scorecard covering customer and business value, flow, adoption, quality, security, reliability, cost, developer experience, technical debt, and AI risk; reports outcomes and investment trade-offs to executive stakeholders. Builds enterprise capability in product engineering, platform engineering, cloud, data, quality, security, reliability, AI engineering, agent orchestration, specification and context engineering, and critical evaluation of AI outputs. Partners with senior Product, Business, Security, Data, Legal, Privacy, Compliance, and Operations leaders to align AI-enabled delivery with customer needs, regulatory obligations, enterprise risk appetite, and commercial priorities. Represents engineering in enterprise planning, investment governance, vendor and strategic-partner decisions, executive risk reviews, and major customer or regulatory engagements. Travel required (up to 25%). QUALIFICATION REQUIREMENTS Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or equivalent practical experience; advanced degree in technology, business, or a related discipline preferred. Typically 15&#43; years of progressive software engineering and technology leadership experience, including 7&#43; years leading leaders and accountability for multiple teams, a major portfolio, or an enterprise engineering capability. Demonstrated success setting enterprise engineering strategy and delivering multi-year transformation across products, platforms, architecture, talent, operating model, and production operations. Proven experience scaling AI-enabled development beyond individual tools or pilots, with measurable outcomes across the lifecycle and operating controls for security, privacy, intellectual property, quality, human oversight, and responsible AI. Executive-level understanding of AI-assisted and agentic engineering, including model and tool evaluation, context and knowledge architecture, AI evaluation methods, LLMOps/MLOps, agent permissions and observability, and AI-specific software supply-chain risks. Strong record of leading Directors, managers, principal-level talent, global teams, vendors, and strategic partners while building succession depth and durable organizational capability. Deep technology breadth across modern architecture, platform engineering, cloud, APIs, distributed systems, data, DevSecOps, quality engineering, observability, reliability, cybersecurity, and regulated production operations. Track record of managing significant budgets, investment portfolios, vendor ecosystems, and build-buy-partner decisions using value, risk, cost, and strategic-fit criteria. </s