Solutions Architect

Technical Lead — AI SDLC & Solutions Architect

Location

Pune, India (Hybrid)

Travel

Minimal to none

Department

AI Practice / Technology Consulting

Employment Type

Full-time

About DynPro

DynPro Professional Services is an AI-led systems engineering and technology consulting firm with over 30 years of delivery excellence. We help enterprise clients accelerate their digital transformation through AI-native engineering, strategic platform partnerships, and rapid, outcome-driven delivery. Our teams work at the intersection of AI strategy, enterprise systems integration, and hands-on engineering — delivering in weeks what traditional firms deliver in months. Learn more at www.dynproindia.com.

Role Overview

We are seeking a high-caliber Technical Lead, AI SDLC & Solutions Architect to join our consulting practice in Pune. This is a high-impact, player-coach role: you will blend deep hands-on coding expertise with a consultative, big-picture vision for software design and solution architecture across a diverse portfolio of enterprise clients.

You will not just advise — you will actively build, prototype, and implement AI-first Software Development Life Cycle (SDLC) systems, and guide clients through the cultural and technical shift from traditional development to high-leverage, AI-augmented engineering.

Key Responsibilities

Hands-On Engineering & Prototyping

• Write production code. Dedicate a significant portion of your time to writing high-quality code, building custom internal AI developer tools, and constructing reusable AI components.
• Build proofs of concept. Rapidly prototype end-to-end AI-native workflows, integration patterns, and custom Large Language Model (LLM) gateways that demonstrate immediate value to clients.
• Implement tooling. Integrate AI-assisted coding tools (e.g., GitHub Copilot, Cursor), prompt engineering frameworks, and retrieval-augmented generation (RAG) workflows into diverse client environments.

Solution Architecture & Systems Design

• Design big-picture architecture. Architect scalable, secure, enterprise-grade AI-native SDLC frameworks tailored to each client's ecosystem.
• Multi-cloud integration. Design robust architectures spanning AWS, Azure, and modern frontend/serverless platforms such as Vercel.
• Agentic workflows. Design and deploy agentic systems in which autonomous AI agents handle complex development lifecycle tasks — multi-agent code reviews, automated unit testing, and legacy system refactoring.
• Enterprise guardrails. Embed compliance, IP protection, security, and bias-mitigation protocols within every AI pipeline you design.

Application Modernization & Legacy Transformation

• Legacy application assessment. Assess monolithic and legacy applications, identify technical debt, modernization candidates, dependencies, and business-critical constraints, and define pragmatic transformation roadmaps.
• Modernization strategy. Lead re-platforming, re-hosting, refactoring, re-architecting, and selective rebuilding initiatives, choosing the right modernization pattern based on business value, risk, cost, and time-to-market.
• Cloud-native transformation. Modernize legacy workloads into scalable cloud-native architectures using microservices, APIs, containers, Kubernetes, serverless patterns, and managed services across AWS and Azure.
• AI-assisted modernization. Apply GenAI and agentic engineering techniques to accelerate code understanding, documentation, dependency analysis, code conversion, refactoring, test generation, and remediation of legacy systems.
• Architecture decomposition. Define domain boundaries and decomposition strategies for monolithic applications, including API enablement, event-driven integration, strangler patterns, and incremental migration approaches.
• Modern engineering enablement. Establish CI/CD, automated testing, observability, security controls, infrastructure as code, and DevSecOps practices to support modernized applications from development through production.
• Migration governance & risk management. Define phased migration plans, architecture guardrails, quality gates, rollback strategies, and measurable success criteria while minimizing business disruption and maintaining security and compliance.
• Value realization. Establish modernization KPIs covering engineering productivity, release velocity, reliability, cloud efficiency, technical-debt reduction, and application maintainability, and communicate outcomes to client leadership.

Client Consulting & Strategic Leadership

• Cross-domain adaptability. Rapidly analyze distinct business domains and translate each client's industry constraints into a specific AI SDLC strategy.
• Technical advisory. Serve as a trusted advisor to client stakeholders (CTOs, VPs of Engineering), communicating complex AI concepts effectively to technical and non-technical audiences.
• AI maturity assessment. Evaluate client engineering organizations, identify SDLC bottlenecks, and define roadmaps for AI engineering automation and Developer Experience (DevEx) optimization.

Qualifications & Requirements

Education

• Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.

Experience

• 10+ years of professional software engineering and software architecture experience.
• 4+ years in technology consulting, professional services, or a client-facing environment.
• Proven track record of architecting and shipping complex, production-grade enterprise software systems.
• Modernization leadership. Demonstrated experience leading legacy application modernization or cloud transformation programs, including assessment, target-state architecture, phased migration, and production cutover.

Technical Skills

• Polyglot developer: Advanced proficiency across modern languages and frameworks, including Python, Java, Node.js, React, Next.js, and Angular.
• Cloud ecosystems: Hands-on architecture experience on AWS (e.g., Bedrock) and Azure (e.g., Azure OpenAI Service), with deployment experience on Vercel.
• GenAI expertise: Deep understanding of foundational LLM frameworks, vector databases, RAG architecture, and fine-tuning / prompt engineering patterns.
• DevOps & infrastructure: Strong mastery of CI/CD pipelines, automated testing, containerization (Docker, Kubernetes), and infrastructure as code.
• Modernization architecture: Strong knowledge of monolith-to-microservices decomposition, API modernization, event-driven architecture, containerization, cloud migration patterns, and incremental transformation approaches such as the strangler pattern.

Consulting Mindset

• Excellent communication, presentation, and storytelling skills, with the ability to manage client expectations and confidently handle pushback.