Jobs at Neuralgo Software India Private Limited

Build scalable, high-performing React.js frontends and reusable UI components for AI-powered products. Integrate REST and GraphQL APIs, optimize performance and browser compatibility, contribute to SSR and full-stack MERN development, own major UI modules, participate in code reviews, mentor junior developers, and influence frontend architecture, component libraries, and UX patterns.

Design, build, fine-tune, deploy, and monitor generative AI and machine learning systems. Develop RAG and LLM solutions, training and inference pipelines, model evaluation workflows, optimization techniques, and production-grade enterprise AI applications. Collaborate with engineering, product, and cross-functional teams while continuously improving model performance, reliability, and scalability.

Lead end-to-end architecture for production-grade AI, ML, and Generative AI solutions on AWS. Design secure, scalable, multi-tenant systems using serverless, microservices, event-driven, RAG, and MLOps patterns. Guide implementation from proof of concept through production, optimize cost and performance, mentor engineering teams, collaborate with clients and stakeholders, and develop roadmaps, proposals, and reusable GenAI platforms.

Develop scalable Python backend services, APIs, packages, SDKs, and integrations for AI-driven platforms. Build RESTful and GraphQL services, work with SQL and NoSQL databases, implement asynchronous and concurrent systems, and improve performance, reliability, and scalability. Contribute to CI/CD, containerization, and cloud deployments while collaborating with frontend, DevOps, and AI/ML teams. The role also includes mentoring junior developers and shaping backend engineering standards.

Designs, builds, and owns scalable AWS-based data platforms and ETL pipelines across client engagements. Responsibilities include translating business requirements into technical solutions, optimizing data integration workflows, ensuring production reliability and data quality, documenting architectures and test plans, supporting deployments and user onboarding, and mentoring junior engineers. The role also involves stakeholder collaboration, agile delivery, unit testing, peer reviews, and implementing modern cloud-native data practices.

Own strategic customer accounts across the full lifecycle, from sales handover and onboarding through delivery, UAT, adoption, renewals, and expansion. Build trusted relationships, coordinate Sales, Delivery, Engineering, and Leadership, manage risks and escalations, align scope with customer outcomes, support AI solution adoption, and identify growth opportunities. Maintain account plans, stakeholder communications, health tracking, business reviews, and customer feedback while improving engagement and delivery practices.

Conduct outbound calls, email, and LinkedIn outreach to named mid-market and enterprise accounts. Qualify customer needs, discuss AI and AWS solutions with senior buyers, convert accounts into qualified opportunities, and hand off conversations to Sales while meeting quarterly opportunity targets.

Build scalable, high-performing React.js frontends and reusable UI components. Integrate REST and GraphQL APIs, optimize performance and data flows, contribute to SSR and full-stack MERN development, participate in code reviews, own major UI modules, mentor junior developers, and collaborate with design, backend, and product teams.

Design and build scalable Python backend services, APIs, packages, SDKs, and integrations for AI-driven platforms. Work with SQL and NoSQL databases, asynchronous programming, REST and GraphQL services, CI/CD, containerization, and cloud deployments. Improve system performance, scalability, and reliability while collaborating with frontend, DevOps, and AI/ML teams. Mentor junior developers and contribute to backend engineering standards.

Design, build, fine-tune, deploy, and monitor generative AI and machine learning systems. Responsibilities include developing RAG and LLM solutions, building training and inference pipelines, evaluating model performance, optimizing models, productionizing research, and collaborating with product and engineering teams on enterprise use cases.