Solution Architect - Agentic Engineering

Responsibilities:

• Lead technical discovery, solution architecture, and engineering delivery for Generative AI, AI Agents, Agentic AI, and custom AI applications.
• Understand business challenges, workflows, constraints, and desired outcomes and translate them into scalable technical solutions.
• Design end-to-end architectures including applications, APIs, services, data platforms, integrations, AI components, cloud infrastructure, and non-functional requirements.
• Evaluate and select appropriate solution approaches across traditional software engineering, workflow automation, RAG, GenAI, single-agent, and multi-agent architectures.
• Define and communicate architectural trade-offs involving scalability, reliability, performance, latency, security, maintainability, complexity, and cost.
• Establish technical standards, reference architectures, reusable patterns, and architecture decision records.
• Conduct architecture reviews, design reviews, and implementation governance to ensure technical coherence.
• Architect production-grade Agentic AI systems including orchestration, agent decomposition, tools and skills, context engineering, workflow state, memory, and human-in-the-loop processes.
• Design RAG and enterprise knowledge architectures including retrieval, hybrid search, reranking, grounding, metadata strategies, taxonomies, and knowledge graphs where appropriate.
• Design agent-to-tool integrations through APIs, event-driven architectures, and interoperability standards such as MCP.
• Define model selection, structured output strategies, model routing, fallback handling, and latency and cost optimization approaches.
• Establish evaluation frameworks covering task success, retrieval quality, groundedness, tool-use correctness, regression testing, and failure-mode analysis.
• Implement guardrails, security controls, observability, tracing, auditability, and responsible AI practices within production systems.
• Apply distributed system and software architecture principles across APIs, services, asynchronous processing, event-driven systems, state management, caching, concurrency, and data flows.
• Design systems for scalability, resiliency, fault tolerance, security, privacy, performance, and operational maintainability.
• Drive engineering excellence through code reviews, design reviews, automated testing, quality gates, and production-readiness assessments.
• Remain hands-on by reviewing code, prototyping complex technical solutions, and supporting engineers with challenging technical issues.
• Guide teams in resolving challenges related to agent behavior, retrieval, integrations, databases, concurrency, latency, and production performance.
• Ensure enterprise production readiness through CI/CD, observability, security, versioning, operational support, and runbook development.
• Monitor and manage technical debt, engineering risks, and production readiness.
• Champion AI-assisted engineering practices across architecture, development, testing, debugging, code reviews, and documentation.
• Drive responsible adoption of AI coding assistants and coding agents while maintaining engineering governance and quality controls.
• Evaluate emerging AI models, frameworks, tools, and engineering practices based on measurable business and technical outcomes.
• Identify opportunities to improve engineering productivity, quality, and delivery velocity through AI-enabled automation.
• Lead engineering teams of approximately 10–20 engineers across multiple delivery initiatives.
• Mentor Technical Leads and engineers in system design, Agentic AI architecture, and production engineering practices.
• Manage hiring, technical assessments, performance management, career development, and capability building.
• Oversee technical capacity planning and skill alignment across programs.
• Develop reusable components, accelerators, reference implementations, and engineering standards.
• Foster a culture of technical ownership, accountability, continuous learning, and engineering excellence.
• Lead client workshops, technical discovery sessions, architecture discussions, and solution reviews.
• Translate business requirements into technical architectures, delivery plans, estimates, dependencies, costs, and risk assessments.
• Communicate complex technical concepts and architectural decisions to engineering, business, and executive stakeholders.
• Manage technical escalations and guide teams toward sustainable resolutions.
• Support presales activities through technical solutioning, architecture development, estimation, and scoping.
• Ensure proposed solutions are technically feasible, commercially realistic, and executable.

Desired Profile:

• Experience: 12–15 years of software engineering experience with significant solution architecture and technical leadership responsibilities.
• Proven experience leading engineering teams and delivering large-scale enterprise technology solutions.
• Deep expertise in system design, software architecture, and distributed systems.
• Demonstrated ability to convert complex or ambiguous business requirements into practical technical architectures and execution plans.
• Significant recent experience designing and delivering production GenAI, AI Agent, or Agentic AI solutions used by real users.
• Strong understanding of agent orchestration, context engineering, RAG, retrieval systems, tools and skills, workflow state management, memory design, human-in-the-loop patterns, and agent evaluation.
• Expertise in grounding techniques, guardrails, observability, tracing, security, latency optimization, inference cost management, fallback strategies, and AI failure handling.
• Strong Python engineering skills with experience in FastAPI, asynchronous programming, automated testing, and production software development practices.
• Strong data architecture knowledge across relational databases, document databases, and AI-oriented data stores.
• Working knowledge of React and TypeScript sufficient to architect and review end-to-end AI-powered applications.
• Strong experience with REST APIs, authentication and authorization, OAuth, OIDC, SAML, and asynchronous or message-based integrations.
• Production cloud engineering experience with AWS, Azure, or GCP, including containers, CI/CD, infrastructure as code, secrets management, and monitoring.
• Experience conducting architecture, design, and code reviews and guiding senior engineers and Technical Leads.
• Direct people-management experience including hiring, mentoring, performance management, and career development.
• Strong client-facing, stakeholder management, estimation, scoping, and escalation management capabilities.
• Excellent written and verbal communication skills with the ability to engage engineering, product, business, and executive audiences effectively.
• Preferred domain expertise in life sciences, pharmaceutical, or healthcare technology environments.
• Familiarity with regulated environments and standards such as MLR, GxP, GDPR, HIPAA, or other healthcare data compliance requirements.
• Experience integrating enterprise platforms such as Veeva, Salesforce, Agentforce, Data Cloud, MuleSoft, Boomi, or similar technologies.
• Knowledge of knowledge graphs, hybrid retrieval methods, and advanced enterprise knowledge architectures.
• Experience with LLM or agent observability platforms, evaluation platforms, and model gateways.
• Familiarity with modern agentic frameworks, SDKs, and interoperability standards.
• Consulting or technology services experience in client solutioning, estimation, scoping, and delivery leadership environments.
• Relevant cloud, architecture, or AI certifications preferred.