Top Artificial Intelligence Jobs in Delhi

The AI Developer II will design, build, and implement AI-driven solutions to streamline government workflows, focusing on integrations and optimizing processes.

As an AI Developer II, you will build AI tools and workflows, optimize customer implementations, and collaborate across teams to enhance public sector services with AI technology.

The Solutions Engineer will develop AI agents, configure automations, lead technical onboarding for enterprise customers, and deliver BI reports to enhance customer success.

The Ecosystem Growth Manager will drive growth by onboarding GPU providers, attracting AI developers, and establishing partnerships for AIVM's decentralized cloud infrastructure.

As a Lead Generation Specialist, you'll identify and qualify high-potential prospects in the Web3 and AI domains, employing outreach methods, CRM management, and market research to drive business development.

The AI Strategist will lead strategic enterprise accounts, shape AI strategies, engage with C-level executives, and collaborate cross-functionally to drive AI adoption.

The role involves using scientific expertise to evaluate AI-driven life science tools, designing criteria for AI solutions, and ensuring alignment with scientific data and customer needs.

Design, build, and scale AI-powered automation workflows using n8n and similar tools. Integrate systems via APIs/webhooks, connect CRMs/marketing/databases/AI models, ensure scalability and reliability, debug and monitor automations, and document solutions while collaborating with business stakeholders.

Design, build, and productionize internal AI-powered applications and workflows. Develop frontends, backend APIs, containerize and deploy via Snowflake SPCS, implement CI/CD, secure access, observability, and integrations with enterprise SaaS.

Build and scale Snowflake-native AI execution layers and orchestrated agent workflows. Implement RAG, embedding pipelines, vector retrieval, context-routing, monitoring, and integrations with enterprise data using SQL, Python, and DBT. Ensure scalable, observable, and governed AI systems across the organization.

Lead design, development, and deployment of scalable ML solutions (recommendations, fraud detection, credit risk) on AWS. Build MLOps infrastructure, monitoring/alerting, support production models (on-call), mentor engineers, and collaborate with product teams to deliver and scale AI-driven features.

Design, build, and own AI-powered full-stack platforms and features from discovery through production. Embed AI into products and workflows, ensure reliability and security, support incidents, and mentor teammates.

Develop and manage AI-enabled full-stack applications, integrating AI models and APIs, while collaborating with teams and researching new technologies.

The Technical Architect for AI/ML will define architecture for AI systems, mentor teams, and align strategies with business objectives, utilizing cloud-native tools and frameworks.

Develop responsive front-end and robust back-end systems, collaborate on AI integrations, maintain databases, and implement DevOps practices. Emphasize on security and rapid prototyping in a dynamic, startup-like environment.

The AI Platform Engineer will design and optimize AI/ML infrastructure, automate pipelines, and manage Kubernetes for effective machine learning model deployment and collaboration.

The Engineering Manager - AI Growth leads engineering for AI product adoption and growth, managing monetisation tech and collaborating across teams for product success.

Lead the QA strategy and architect automation frameworks for AI products. Design test approaches for AI models, influence teams, and drive quality initiatives.

Design, provision, and manage scalable cloud infrastructure and Kubernetes clusters for an Agentic AI platform. Build and operate IaC (Terraform), CI/CD pipelines, observability, autoscaling, and release strategies, and provide operational support for large GPU-backed ML workloads. Improve reliability, deployment velocity, and performance of production systems.

As a Solutions Architect, you'll help organizations utilize Generative AI applications effectively, guiding them from strategy through prototype to production and ensuring tangible business value.

Lead client-specific QA programs for generative AI data: define sampling and audits, run root-cause analyses and CAPAs, plan rater training/certification, maintain performance dashboards, manage escalations, standardize SOPs, pilot automations, coach teams, and ensure data compliance and security.

As a Senior AI Engineer, you will design and implement scalable AI systems, mentor junior engineers, and ensure impactful, ethical AI solutions. Responsibilities include collaborating with stakeholders and fine-tuning generative AI models.

Design, build, and deploy LLM-powered agentic and conversational AI for healthcare, including voice automation (IVR/ASR/TTS), RAG architectures, HIPAA-compliant systems, EHR integrations, developer enablement, and mentoring of junior engineers.

Design, build, and deploy GenAI and agentic AI solutions using LLMs and hyperscaler services. Implement RAG pipelines, fine-tune models, manage backend integrations, ensure scalability, security, and production readiness, and collaborate with cross-functional teams to translate business needs into AI systems.

Design, build, and productionize scalable AI/ML pipelines and models (including LLMs and Generative AI). Deploy and optimize models across cloud and edge, implement MLOps (CI/CD, versioning, monitoring), and collaborate with data scientists and engineering teams to deliver reliable, low-latency AI solutions.