Sr Architect

Syneos Health, Inc.; India; On-site; INR 1,800,000 - 2,800,000; Full time

Job summary

Syneos Health is seeking a senior AI-enabled architecture leader in India to drive end-to-end architecture for automation initiatives. You will translate business needs into technical designs, ensuring production-grade scalability, security, and observability across platforms.

Responsibilities include defining patterns for generative AI, managing costs, and partnering with stakeholders to establish ownership and operational models while evaluating build/buy options for long-term success.

Qualifications

• Experience leading AI-enabled architecture initiatives.
• Ability to translate business needs into technical designs.
• Strong understanding of production scalability and security.

Responsibilities

• Lead end-to-end architecture for AI-enabled and automation initiatives, spanning applications, data and knowledge, models, orchestration, agents, APIs, integration, cloud services, identity, security, observability, and operations.
• Translate business outcomes, functional needs, non-functional requirements, and regulatory obligations into architecture options, technical designs, and implementation guidance.
• Design for production scalability, resilience, performance, interoperability, maintainability, portability, and reuse, including workload sizing, capacity assumptions, failure modes, service limits, and operational dependencies.
• Define and develop practical patterns for generative AI, machine learning, intelligent workflows, agentic automation, retrieval and context, tool integration, human oversight, and exception handling when relevant to the project.
• Make cost a first-class architecture concern by assessing consumption drivers and creating cost scenarios for prototype, launch, and scaled adoption.
• Embed telemetry, usage metering, cost attribution, budgets or thresholds, performance monitoring, and operational alerts into the solution design.
• Evaluate build, buy, reuse, and retire options with business value, long-term operability, and security considerations.

Skills

AI architecture

Cloud services

Security

Observability

Tools

AI platforms

Job description

Syneos Health® is a leading fully-integrated life sciences services organization built to accelerate customer success. We partner with innovators at every point across the drug development and commercialization continuum, helping them navigate complexity, anticipate change and accelerate progress.

Every day we perform better because of how we work together, as one team, each the best at what we do. We bring together talented experts across a broad spectrum of business critical corporate functions. Every role plays an essential part in enabling our customers to achieve their goals. Our teams are agile, collaborative, and committed to delivering-for each other, for our customers, and ultimately for the people who rely on the services we support.

Discover what your 25,000 future colleagues already know:

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 building an inclusive culture - where you can authentically be yourself. Central to this is our purpose - Driven to Deliver - which captures the passion of our colleagues to show up each day and shape solutions that have the ability to dramatically impact someone's life.
• We are continuously building the company we all want to work for and our customers want to work with. Why? Because we know that when we bring together smart colleagues from across the world, we can shape the future of healthcare, driving impact for customers and defining the pace of patient progress.

Job Responsibilities

AI-enabled solution architecture

• - Lead end-to-end architecture for assigned AI-enabled and automation initiatives, spanning applications, data and knowledge, models, orchestration, agents, APIs, integration, cloud services, identity, security, observability, and operations.
• - Translate business outcomes, functional needs, non-functional requirements, and regulatory obligations into architecture options, technical designs, and implementation guidance.
• - Design for production scalability, resilience, performance, interoperability, maintainability, portability, and reuse, including workload sizing, capacity assumptions, failure modes, service limits, and operational dependencies.
• - Define and develop practical patterns for generative AI, machine learning, intelligent workflows, agentic automation, retrieval and context, tool integration, human oversight, and exception handling when relevant to the project.

AI scalability, economics & operational readiness

• - Make cost a first-class architecture concern by assessing consumption drivers such as model usage, tokens, compute, storage, data movement, licensing, vendor services, and support effort.
• - Create cost and capacity scenarios for prototype, launch, and scaled adoption; identify material cost-performance-quality trade-offs; and recommend fit-for-purpose models, hosting patterns, caching, routing, batching, quotas, and scaling controls.
• - Embed telemetry, usage metering, cost attribution, budgets or thresholds, performance monitoring, quality evaluation, and operational alerts into the solution design so teams can make evidence-based scale, optimize, or stop decisions.
• - Partner with platform, FinOps, operations, and product stakeholders to establish ownership, service expectations, support models, lifecycle controls, and continuous optimization practices before production adoption.
• - Evaluate build, buy, reuse, and retire options using business value, total cost, architecture fit, delivery risk, security, compliance, vendor dependency, and long-term operability.

Automation advancement across core technology

• - Identify and shape high-value automation opportunities across Enterprise Architecture and Enterprise Technology & Platforms, to streamline, simplify and elevate intelligence related to complex technical service delivery processes.
• - Advance automation-as-a-capability by designing reusable workflows, APIs, events, templates, policy-as-code, architecture-as-code, and paved-road patterns that reduce manual effort and improve consistency.
• - Lead focused proofs of value and production pilots with clear hypotheses, success measures, guardrails, and exit criteria; convert validated learning into reusable patterns, backlog recommendations, standards, or scaled implementation plans.
• - Promote human-centered automation that preserves appropriate review, accountability, transparency, and fallback paths for material decisions and regulated processes.

Architecture governance, security & responsible AI

• - Apply enterprise architecture principles, approved technology standards, security and privacy requirements, data governance, responsible AI expectations, and regulatory controls throughout the delivery lifecycle.
• - Apply project guardrails for data handling, model and provider use, identity an