Top Artificial Intelligence Jobs in Delhi

Lead technical consultant responsible for the full software development lifecycle, including technical design, implementation, testing, deployment, documentation, and support. Builds enterprise AI and LLM applications using OpenAI, Anthropic, Gemini, agent frameworks, Java, Spring Boot, Python, microservices, databases, containers, Kubernetes, and cloud platforms. Collaborates with architects and developers to translate designs into technical solutions while ensuring security, resiliency, and enterprise architecture standards.

Leads technical planning, requirements gathering, estimation, architecture, development, testing, deployment, documentation, and support for client applications. Mentors developers, provides technical leadership, collaborates with solution architects, translates designs into technical requirements, manages projects, and communicates with technical and non-technical stakeholders in a consulting environment.

Lead technical consultant responsible for designing, developing, testing, deploying, documenting, and supporting enterprise applications. The role focuses on generative AI and LLM integrations using OpenAI, Anthropic, Gemini, prompt engineering, fine-tuning, and agent frameworks. Responsibilities also include Java and Spring Boot backend development, Python, Oracle, microservices, Docker, Kubernetes, cloud platforms, security, resiliency, and enterprise architecture. Requires 8+ years of software development experience and strong AI/LLM application expertise.

Lead technical consultant responsible for designing, developing, testing, deploying, documenting, and supporting enterprise applications. The role focuses on generative AI and LLM integrations using OpenAI, Anthropic, Gemini, agent frameworks, Java, Spring Boot, Python, microservices, databases, containers, Kubernetes, and cloud platforms. The consultant collaborates with architects and developers to translate designs into technical solutions and provides expertise in security, resiliency, and enterprise architecture.

Own the strategy and roadmap for Netomi’s enterprise AI Agents platform. Partner with customers, engineering, research, data science, and go-to-market teams to prioritize and ship capabilities. Build agent prototypes, connect APIs and MCP tools, design evaluations, analyze model quality, debug production failures, and establish governance, observability, and rollout practices. Drive measurable outcomes including resolution rate, cost, CSAT, adoption, and revenue expansion.

Lead capture and translation of business requirements for Salesforce Sales Cloud and Experience Cloud. Partner with business leaders to define system goals, prioritize scope, drive implementations, and produce functional and technical designs for engineering teams. Support CRM evolution, agile delivery, and top-of-funnel processes like lead, account, and partner management.

Build data infrastructure and reliable pipelines, analyze product usage, train traditional and LLM-based models, define product metrics, run experiments, and translate findings into product and go-to-market decisions. The role combines data science, data engineering, product analytics, machine learning, and stakeholder communication, with ownership across data modeling, evaluation, experimentation, and product insight generation.

Conduct STEM and emerging-technology workshops, demonstrations, presentations, and hands-on learning activities for school audiences. Research innovation trends, develop educational content and programs, demonstrate robotics, AI, ML, and maker-space concepts, and collaborate with internal teams. The role requires strong public speaking, mentoring, communication skills, a personal laptop, bilingual English and Hindi proficiency, and willingness to travel for school visits and workshops.

Design, build, and deploy production generative AI features, including LLM applications, RAG pipelines, and agentic systems. Develop scalable Python and cloud backend services, evaluate model accuracy and performance, optimize reliability and cost, and implement monitoring, security, and observability. Collaborate with product, design, and engineering teams while mentoring teammates and exploring emerging AI technologies.

Lead and scale an AI-driven creative design team producing performance marketing assets for paid media. Own creative direction, production workflows, quality control, testing, and performance-driven iteration. Develop AI tool expertise, prompt and style libraries, and automated workflows with engineering partners. Collaborate with marketers, media buyers, copywriters, producers, and video editors while remaining hands-on with design and AI tools.

Build and deploy production AI systems, agents, APIs, backend services, data pipelines, and models. Work directly with customers to translate business challenges into technical solutions, automate workflows, run experiments, measure outcomes, and iterate quickly. The role requires strong software engineering and system design fundamentals, AI knowledge, cloud or Kubernetes familiarity, comfort with ambiguity, and strong communication skills.

Lead the vision, strategy, and execution of HighLevel’s Email AI platform. Responsibilities include generative email content and layout creation, autonomous lifecycle sequence generation, predictive send optimization, AI governance, privacy safeguards, roadmap development, instrumentation, and cross-functional alignment. The role aims to accelerate campaign creation, improve engagement, and launch Email AI and Sequence AI capabilities at scale.

Design and build production AI-powered analytics: conversational interfaces, LLM agents, semantic data models, and scalable data pipelines. Partner with Product and Engineering to deploy RAG/MCP-based solutions, integrate AI with enterprise data and cloud platforms, mentor teams, and promote Responsible AI practices to improve decision-making and automation across the company.

Manage digital-native customer accounts throughout the sales lifecycle, from pipeline generation and complex deal strategy through deployment and renewal. Own revenue targets and forecasts, develop account plans, analyze account and market intelligence, and advise C-level stakeholders on AI adoption. Collaborate with solutions, engineering, marketing, finance, product, and operations teams to deliver customer programs, influence product strategy, and support team growth.

Design and develop scalable, resilient cloud architecture integrated with on-premises systems. Drive technical direction, identify platform efficiency opportunities, communicate complex architecture, and implement changes that improve reliability and supportability. Own critical software projects through design, implementation, quality assurance, deployment, and monitoring while providing technical leadership to engineers.

Conduct independent research on frontier AI evaluation and auditing. Design rigorous evaluations, audit methods, and reproducible experimental pipelines; analyze model capabilities, risks, robustness, and alignment-relevant behavior. Produce audit reports, recommendations, publications, tools, and presentations while collaborating with technical, policy, engineering, and standards teams. The role requires careful handling of confidential information and may involve pilot audits with frontier AI companies.

Lead international expansion of AI-enabled agricultural weather forecasting programs. Responsibilities include selecting new markets, establishing offices and teams, developing government and telco partnerships, overseeing forecast deployment, managing staff and consultants, and building replicable best practices. The role reports to the VP of Agriculture, manages senior personnel, and requires extensive collaboration with governments, researchers, delivery partners, and private-sector organizations across low- and middle-income countries.

Writes high-quality long-form content about AI, enterprise technology, infrastructure, and related topics. Responsibilities include researching complex subjects, developing thought leadership, whitepapers, reports, case studies, website content, and marketing assets; interviewing subject-matter experts; adapting content for varied audiences; editing for accuracy and clarity; and collaborating with marketing and SEO teams.

Lead Multiplier’s company-wide AI strategy, shared AI platform, and customer-facing AI experiences. Build agent infrastructure, retrieval, tool access, evaluation, and safety systems; launch an AI agent that answers questions using customer data and eventually executes tasks. Lead a cross-functional team, establish AI development standards, and partner across product, engineering, support, and operations to deliver trusted, scalable AI capabilities.

The role involves developing full-stack web apps, creating APIs, improving lead capture models, and collaborating across teams to enhance the Social Planner product.

Edit and produce short- and long-form videos for social media, marketing, training, and promotional campaigns. Use AI-powered tools to create voiceovers, avatars, effects, subtitles, and visual content. Optimize videos for multiple digital platforms while maintaining quality and brand consistency. Collaborate with marketing, content, and design teams, deliver projects on schedule, and stay current with AI video editing trends.

Edit paid ads, social videos, UGC, product videos, podcasts, and website assets from raw footage through final delivery. Create fast-paced, high-retention short-form content, repurpose long-form footage, and produce multiple ad variations for testing. Use AI tools for voiceovers, captions, transcript editing, audio cleanup, reframing, clipping, and creative variations. Collaborate with social and creative teams to improve performance and stay current with AI editing tools and video trends.

Build and deploy production AI automations across accounting, compliance, sales, marketing, HR, and finance. The role covers discovery, coding, deployment, monitoring, debugging, support, ROI measurement, and business handover. The engineer will develop LLM-based systems, integrations, agents, and workflows using Python, APIs, databases, warehouses, and business tools. They will partner with non-technical stakeholders, independently own systems end to end, train business users, maintain infrastructure, and identify high-value automation opportunities.

Build and productionize machine learning, LLM, and AI solutions with Data Science teams. Design industrialized data-processing pipelines, implement MLOps and LLMOps frameworks, manage model lifecycles, and develop production Python, ML, and cloud solutions using Azure, GCP, Databricks, Spark, and PySpark. Gather requirements, estimate work, document solutions, present results, and support technical best practices.

Build and operate production AI systems for financial products, including LLM pipelines, RAG, document extraction, anomaly detection, model serving, and automated reconciliation. Develop reliable backend integrations, vector search infrastructure, monitoring, evaluation, observability, and human-in-the-loop workflows. Collaborate with product, backend engineering, and data science teams to establish scalable AI engineering practices.