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