Technical Lead- AI SDLC & Solutions Architect

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. 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 8+ years of professional software engineering and software architecture experience. 2+ years in technology consulting, professional services, or a client-facing environment. Proven track record of architecting and shipping complex, production-grade enterprise software systems. 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. Consulting Mindset Excellent communication, presentation, and storytelling skills, with the ability to manage client expectations and confidently handle pushback. Performance Metrics & KPIs Technical & Engineering Excellence Code contributions: Consistent volume of high-quality code pushed to internal asset libraries and client repositories (reusable AI boilerplates, LLM gateways). Architecture quality: 100% adherence to security, compliance, and IP-protection guardrails in all designed AI pipelines. Velocity acceleration: Measurable reduction in client setup and deployment time for new AI toolchains using your blueprints. Consulting & Client Success Client satisfaction (CSAT/NPS): High satisfaction scores on technical delivery and strategic guidance from client engineering stakeholders. Retention and expansion: Guide clients to their initial milestones, paving the way for renewals and expanded scope. Cross-domain adaptability: Transition architectures and code solutions across client domains (e.g., Java/Azure financial backend to Next.js/AWS/Vercel retail stack) with minimal friction. Innovation & Practice Enablement Asset reusability: Develop at least 2–3 architectural blueprints or code templates actively adopted by other engineering teams within the firm. Upskilling impact: Number of internal engineers trained or certified through your workshops on AI-native SDLC tools.