Top Artificial Intelligence Jobs in Mumbai

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.

Lead the design, development, and operation of enterprise Agentic AI and ML platforms. Build agent orchestration workflows, APIs, custom connectors, integrations, monitoring, observability, and reliable cloud-based systems. Establish LLMOps, MLOps, governance, security, and lifecycle-management practices; troubleshoot complex platform issues; lead technical prototypes; document architectures; and mentor engineers. The role focuses on scalable Agentic AI solutions using cloud, orchestration, and modern ML tooling.

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.

Build Python-based tools for layout, animation, matchmove, and previs artists working with complex CG scenes. Develop scene-management systems, asset and animation library integrations, and 3DEqualizer data import/export workflows. Collaborate with product owners and stakeholders in an Agile environment, contributing across design, development, maintenance, and pipeline integration. Work may include front-end and back-end development for high-end VFX and feature animation pipelines.

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.

Design and build reinforcement-learning training environments and diverse tasks to evaluate and improve LLM agents; iterate rapidly on task designs from customer feedback, deliver high-quality outputs with minimal supervision, and maintain PST overlap for collaboration.

Create realistic computer-use tasks for AI agents on Linux desktops. Responsibilities include designing multi-step scenarios, preparing files and documents, writing agent instructions, developing Python setup and scoring scripts, testing environments, and documenting tasks. Requires Python, daily AI tool usage, basic AI/ML experience, Ubuntu and virtual machine familiarity, attention to detail, and Git basics. Experience building RL environments or computer-use benchmarks is preferred.

Manage APAC mid-market accounts, develop regional go-to-market strategies, win lighthouse customers through outbound sales, and drive consumption revenue. Partner with solutions, research, engineering, and channel partners on complex customer programs. Responsibilities include revenue forecasting, executive engagement, customer events, teammate onboarding, and helping build scalable sales processes for an AI-native organization.

Drive strategic growth across key accounts by identifying new business, expanding customer relationships, and aligning SUSE’s Linux, Kubernetes, cloud, edge, AI, and security solutions with client objectives. Manage consultative enterprise sales cycles from prospecting through negotiation, closing, and customer handoff. Maintain CRM data, forecast pipeline, develop account plans, monitor competitors, pursue upsell and cross-sell opportunities, and collaborate with presales, partners, specialists, and customer success teams.

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.

Provides physical and electronic security consulting for projects across the Middle East. Designs integrated security systems, physical site hardening, surveillance, access control, intrusion detection, and networked solutions. Applies security standards, defense-in-depth, and CPTED principles; produces AutoCAD and BIM documentation; prepares proposals and client presentations; and manages project deliverables, schedules, budgets, and coordination with other disciplines. Requires extensive security design and proposal-writing experience, strong communication skills, and travel across the region.

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 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 enterprise-grade LLM applications using RAG and agent frameworks. Design LangGraph state machines with conditional routing and parallel execution, develop retrieval pipelines for complex unstructured documents, tune embeddings and vector search, and apply strong software engineering practices across asynchronous Python, REST APIs, microservices, and Git workflows.

Develop and support enterprise AI and software solutions end to end, from technology selection and architecture through development, deployment, infrastructure management, CI/CD, database administration, identity integration, and production support. Build LLM, RAG, and agentic AI applications; establish engineering standards and documentation; troubleshoot across application and infrastructure layers; and enable collaborative development at scale.

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 maintain AI-native full-stack applications across frontend and backend systems. Develop modular React interfaces, REST APIs using Python or JavaScript/TypeScript frameworks, and work with SQL or NoSQL persistence. Set up application stacks on cloud providers, contribute throughout the product lifecycle, apply sound software design principles, and continuously learn and share knowledge.

Build and deploy AI-powered automation across customer service, marketing, compliance, and internal operations. Develop scripts, workflows, dashboards, reporting, reconciliations, and KYC/AML decisioning solutions using SQL, scripting, low-code tools, AI, and LLMs. Identify operational inefficiencies, streamline compliance processes, and deliver data-driven tools that improve productivity, accuracy, and customer experience.

Build and deploy enterprise Generative AI applications using LLMs, RAG pipelines, vector databases, multimodal models, and autonomous agents. Develop prompt flows, benchmark and fine-tune models, implement MLOps practices, deploy solutions across major cloud platforms, and integrate AI capabilities into APIs and microservices. Collaborate with consultants, data scientists, engineers, architects, and customers to deliver secure, scalable, compliant, and cost-effective GenAI solutions.

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.

Lead pre-sales and technical delivery for Microsoft Industry Solutions, mapping customer needs to Microsoft Cloud and AI solutions. Drive project planning, risk mitigation, stakeholder engagement, IP reuse, and business development while mentoring teams and ensuring operational compliance.

Own product features end-to-end, including architecture, integrations, technical specifications, deployment, testing, and production reliability. Use AI agents as the primary development tool, delegate implementation, critically review generated code, and ensure financial transaction reliability. Work independently as an individual contributor while helping evolve the company’s AI-first development process.

Designs end-to-end AI and generative AI solutions, leads RFP responses, develops proposals and sales collateral, conducts client workshops, and engages executive stakeholders. The role establishes AI governance, compliance, and observability frameworks; partners with engineering teams on delivery; and manages and mentors solution architects, data scientists, and engineers. It requires strong expertise in GenAI stacks, RAG, multi-agent systems, LLMOps, responsible AI, consulting, and enterprise pre-sales.

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.