DataNeuron
Website:
dataneuron.ai
Job details:
Job Title: Solution Architect (GenAI)
Location: Noida (Hybrid)
Experience: 2.5 – 4 years
Employment Type: Full-time
About the Role
We’re looking for a hands-on Solution Architect (GenAI) to lead end-to-end solution design and delivery for real-world GenAI use cases.
You will work closely with sales, product, and engineering teams to take solutions from POC to production, with a strong focus on enterprise-grade deployments using the DataNeuron platform.
Key Responsibilities
- Lead end-to-end solution architecture, technical design, and deployment of enterprise GenAI applications on the DataNeuron platform
- Drive pre-sales and customer engagements including solution discovery, technical demos, architecture discussions, POCs, solution scoping, and technical validations.
- Translate business requirements into scalable, secure, and production-ready GenAI architectures with strong focus on performance, reliability, and enterprise deployment standards.
- Build and support customer integrations, onboarding, and production deployments involving APIs, data pipelines, cloud infrastructure, enterprise systems, and AI orchestration workflows.
- Design and document reusable reference architectures, implementation frameworks, runbooks, and best practices for scalable GenAI and agentic AI systems.
- Collaborate closely with product, engineering, and customer teams to gather feedback, improve platform capabilities, and drive successful AI solution delivery.
- Ensure deployed solutions meet enterprise requirements around scalability, observability, security, compliance, governance, and SLA expectations.
Required Qualifications
- 2.5–4 years of experience in Solution Architecture, ML/NLP Engineering, Platform Engineering, Applied AI, or GenAI roles with strong hands-on development experience.
- Strong experience building and deploying production-grade GenAI applications using LLMs, prompt engineering, RAG pipelines, agentic AI systems using Langchain,A2A, LLM fine-tuning concepts, and AI orchestration frameworks. Exposure to open-source models and enterprise AI deployments is required.
- Strong understanding of Data Science, Machine Learning, and LLM fundamentals including model evaluation, retrieval techniques, data pipelines, inference optimization, performance tuning, and core LLM concepts.
- Strong understanding of backend engineering and system design concepts including APIs, microservices, distributed systems, asynchronous processing, workflow orchestration, caching, event-driven systems, and scalable architecture patterns.
- Strong programming skills in Python along with good understanding of databases, data modeling, and query optimization concepts.
- Experience integrating ML/LLM systems with cloud platforms such as AWS, Azure, or GCP along with enterprise data sources and APIs.
- Good communication and stakeholder management skills with the ability to build demos, run POCs, engage with customers, and clearly communicate technical concepts and trade-offs to both technical and non-technical audiences.
Nice to Have
- Experience working with low-code/no-code AI platforms and prior experience in product-based technology companies.
- Good understanding of Knowledge Graph implementations and handling different types of structured and unstructured data formats.
- Exposure to model evaluation, fine-tuning, distillation, and data-centric AI approaches.
- Understanding of enterprise AI governance, security, compliance, and responsible AI best practices.
What You’ll Get
- High ownership role with direct impact on product and customers
- Opportunity to build production-grade GenAI systems for enterprise use cases
- Close collaboration with a focused team shaping real-world AI deployments
- Strong influence on product direction and customer success frameworks
Click on Apply to know more.