We are looking for an Agentic Solution Architect who can set technical direction, guide engineering teams, partner with clients, turn ambiguous goals into scalable agentic solution patterns, and deliver production-ready autonomous systems in real client environments. SoftServe’s Autonomous Agentic Platform, Neo, represents the next evolution in software engineering: an ecosystem where agents orchestrate agents, workflows adapt dynamically, and guardrails enable reliable autonomy at scale.
Responsibilities
Help shape the technical direction of a production-grade Autonomous Agentic Platform
Design and evolve reusable agentic engineering patterns, contracts, interfaces, workflows, and delivery slices for Agentic SDLC execution
Drive implementation strategy across agent workflows, MCP tools, context pipelines, orchestration patterns, and platform services
Partner with architects, product owners, and client stakeholders to turn vision into executable technical systems
Make high-impact technical decisions that balance speed, quality, scalability, reliability, and client delivery needs
Evolve engineering standards for code quality, testing, observability, documentation, operational readiness, and agentic workflow governance
Mentor technical leads and engineers by clarifying architecture, resolving design questions, and raising the quality bar
Use metrics, benchmarking, and validation evidence to improve platform reliability, agent performance, and delivery quality
Build practical processes and tooling that help teams execute agentic engineering at scale
Contribute to Neo’s technical voice through papers, conference talks, client-facing thought leadership, and reusable internal enablement for agentic engineering
Requirements
A systems thinker with experience designing software architectures and translating complex requirements into scalable technical solutions
Hands-on experience across our core agentic engineering stack: Python and TypeScript/JavaScript; React and Node.js; REST/OpenAPI service contracts; MCP tool integration; vector databases, retrieval architectures, and knowledge graph concepts; cloud-native deployment patterns; CI/CD; observability; and automated testing
Experience with AI/ML engineering and LLM-based systems, including agentic workflows, retrieval and indexing strategies, context engineering, evaluation frameworks, model orchestration, and managing context, latency, cost, and quality tradeoffs
Fluent in partnering with AI agents and agentic workflows in daily engineering execution
Grounded in architecture fundamentals such as microservices, C4 modeling, design patterns, and contract-first engineering, with the ability to apply those foundations to autonomous agentic systems
Able to turn business and technical goals into solution architecture, technical contracts, execution models, and implementation plans
Comfortable guiding engineers through complex technical decisions, implementation tradeoffs, and delivery risks
Evidence-driven in technical decision-making, using metrics, benchmarking, validation results, and system behavior to improve architecture and solution quality
A strong communicator who can align engineers, architects, product stakeholders, executives, and client teams around technical direction and delivery priorities
Motivated to explore emerging agentic engineering patterns and bring practical innovation to clients