Jobs at Neuralgo Software India Private Limited

Lead complex technology projects from planning through closure, managing scope, budgets, schedules, resources, dependencies, risks, governance, and stakeholder communication. Coordinate engineering, product, QA, operations, consulting teams, vendors, and clients. Manage Agile execution, documentation, compliance, executive reviews, and government or public-sector engagements. Mentor teams, improve delivery processes, support pre-sales and proposals, and ensure predictable, high-quality project outcomes.

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.

Lead end-to-end architecture for production-grade AI, machine learning, and Generative AI solutions on AWS. Design secure, scalable, multi-tenant systems using cloud-native, serverless, microservices, event-driven, MLOps, and RAG patterns. Guide implementations from proof of concept through production, optimize performance and costs, lead architecture reviews and client workshops, develop roadmaps and proposals, and mentor ML, Python, and cloud engineering teams across multiple engagements.

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.

Manage employee onboarding and offboarding, Microsoft 365 and Entra ID administration, IAM controls, Windows and macOS endpoint management, SaaS applications, IT support, workflow automation, networking, documentation, and security compliance. The role also handles IT asset inventory, procurement support, onboarding kits, vendor coordination, facilities, office supplies, and general administration.

Supports presales solutioning for GenAI, LLM, agentic AI, and AI/ML platform opportunities. Responsibilities include customer requirements discovery, workflow and pain-point documentation, solution narrative development, RFP/RFI responses, proposal writing, solution presentations, and collaboration with Sales, architects, engineers, and consultants. The role progressively owns proposal narratives, maps business requirements to AI solutions, and develops reusable use cases and architectural patterns.

The Business Analyst will gather and manage requirements for government and PSU digital transformation projects, document business and functional specifications, map workflows, track scope and changes, and maintain requirement traceability. The role bridges government stakeholders with development, testing, product, and delivery teams; supports UAT, issue resolution, approvals, and client sign-offs; and creates user manuals, SOPs, training materials, and knowledge-transfer content. It also facilitates training, demonstrations, stakeholder meetings, and continuous improvements to governance and documentation standards.

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.

Lead complex AI and data projects from planning through delivery, managing scope, resources, milestones, dependencies, risks, budgets, and timelines. Coordinate engineering, data science, product, QA, customers, and external stakeholders. Facilitate Agile ceremonies, maintain documentation, resolve blockers, ensure client alignment and satisfaction, mentor cross-functional teams, improve delivery processes, and support proposals, presales, and customer onboarding.