Solution Architect - Agentic Engineering

Job description

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 Ty