Top Artificial Intelligence Jobs in Pune

Leads global accounting operations, including cash accounting, general ledger reconciliations, journal entries, foreign exchange analysis, variance analysis, and month-end close. Supports corporate and statutory reporting, audits, SOX compliance, risk mitigation, process improvements, entity transitions, and acquisitions. Collaborates with international stakeholders, addresses accounting queries, prepares reports and dashboards, and drives automation and AI adoption for greater efficiency.

Leads the architecture, design, coding, testing, and delivery of scalable, secure, cloud-native software and data distribution platforms. Defines modernization strategy, builds APIs, services, data pipelines, and analytical capabilities, and drives architecture and design reviews. Coordinates globally distributed teams, partners across engineering and business functions, mentors engineers, and promotes reliability, observability, continuous delivery, and engineering excellence.

Drive enterprise software acquisition and revenue growth across a regional territory. Manage prospects and a small existing customer base using land-and-expand strategies, executive engagement, demonstrations, events, territory planning, and strategic account development. Build relationships with VP and C-level stakeholders, partners, and internal teams to close complex sales, acquire new logos, expand usage, and support successful customer implementations.

Own roadmap execution and product requirements for Mastercard’s global disputes platform. Manage the agile backlog, define epics and user stories, prioritize capabilities, and partner with engineering, architecture, data science, operations, and regional stakeholders. Balance global platform consistency with localized market needs, support AI-powered automation, facilitate discovery, guide releases, monitor outcomes, and drive adoption and continuous improvement across markets.

The Senior Solutions Architect advises Professional Services teams on complex Edwin AI implementations, including customer discovery, solution design, integrations, estimates, statements of work, deployment oversight, troubleshooting, and technical escalations. The role defines delivery best practices, mentors regional teams, ensures consistent global delivery, collaborates with product and project teams, contributes customer feedback to the product roadmap, and supports professional services package development. Travel of up to 25% is required.

Design, build, and deploy AI and Generative AI solutions using LLMs, agent frameworks, and RAG. Develop scalable AI/ML pipelines, cloud-native integrations (Azure/Databricks/AWS), and MLOps/LLMOps deployments. Integrate AI with APIs, databases, and internal apps. Implement Responsible AI practices (bias detection, hallucination mitigation, explainability, safety guardrails). Monitor, test, and maintain production AI systems while staying current on Generative AI trends.

Own production operations for AI agents, including monitoring accuracy, latency, SLAs, drift, guardrails, and reliability. Provide L1/L2 support, investigate traces and root causes, evaluate reasoning and retrieval quality, manage API and authentication issues, and partner with AI Engineering on prompt, routing, and context improvements. Embed governance, approval workflows, and audit logging into the agent learning lifecycle.

Own and execute AI product areas for Mastercard’s Payment Performance Intelligence platform, from discovery and technical requirements through launch and iteration. Partner with Data Science, Engineering, Platform, Commercial, Risk, Legal, Security, and Compliance teams to develop GenAI, chatbot, agentic service, predictive insight, and self-service analytics capabilities. Manage roadmaps, backlogs, prioritization, success metrics, stakeholder communications, and responsible AI requirements while mentoring junior product professionals.

Design and implement production-grade AI and machine learning solutions, including scalable GenAI applications, RAG pipelines, ML platforms, data pipelines, and distributed cloud systems. Collaborate with architects and client-facing teams, write tested and maintainable code, conduct technical research and proofs of concept, participate in Agile delivery, troubleshoot production issues, review code, estimate work, and mentor engineers.

Own customer success across an assigned city, including clinic launch events, onboarding, staff training, workflow configuration, engagement, retention, institutional partnerships, screening camps, and community outreach. The role requires regular interaction with dental clinics and healthcare stakeholders, on-site presence, and territory-level ownership of client relationships and performance targets.

Leads Workday integrations and packaged application implementations, including customization, module implementation, enterprise application integration, and development using Workday Studio, EIB, Core Connector, and Workday Extend. Builds and deploys custom Workday applications, coordinates cross-functional product launches, establishes platform best practices, and supports adoption. Applies expertise in HR, payroll, and compensation while exploring AI, machine learning, and intelligent automation capabilities.

Analyzes large datasets to identify trends, correlations, data-quality issues, and actionable business insights. Builds data pipelines, logical data models, interactive dashboards, visual reports, and automated reporting processes. Partners with stakeholders to recommend operational improvements, strengthen controls, and support data-driven decisions. The role also involves data integration, engineering, analytics, machine learning fundamentals, and payments-domain knowledge.

Leads the global ERP Solution Centre of Excellence, setting ERP strategy, process architecture, governance, solution roadmaps, and functional delivery. Oversees SAP and Axapta consultants, architects, and domain experts; leads the Functional Design Authority; guides investment and design decisions; and ensures alignment with architecture, security, data, integration, controls, validation, and regulatory standards. Advises senior stakeholders and advances ERP transformation, automation, analytics, and emerging technology adoption.

Provide production support for critical banking technology infrastructure, rapidly resolve incidents, participate in major incident management, and improve application-support efficiency. The role involves Linux/Unix administration, Java and database technologies, middleware, microservices, containers, cloud platforms, monitoring, scheduling, CI/CD, and messaging tools. Responsibilities also include stakeholder communication, risk and control management, technical issue resolution, operational support during shifts and releases, and occasional weekend coverage.

Lead the AI Quality engineering team while building evaluation infrastructure, observability tooling, datasets, annotation workflows, and diagnostic systems for production AI agents. Design CI/CD evaluation pipelines, run experiments across prompts and models, diagnose agent failures, improve latency and cost, and partner with AI Core engineering. This is a hands-on player-coach role requiring production software experience and expertise in evaluating complex, tool-using LLM systems.

Analyze and improve data quality, lineage, controls, and authoritative sources. Build data pipelines, logical data models, dashboards, reports, and automated analytics. Apply statistical, machine learning, and advanced analytical techniques to identify trends, risks, anomalies, and improvement opportunities. Translate complex findings into actionable recommendations for senior stakeholders while ensuring data governance, privacy, security, regulatory compliance, and reporting accuracy. Partner across business, technology, risk, finance, operations, and data teams, potentially leading analytical workstreams.

Leads large-scale technology consulting and digital transformation programs for Life Sciences clients. Responsibilities include solution strategy, delivery governance, stakeholder management, enterprise data and AI implementations, client advisory, proposal development, account expansion, and global team leadership. The role requires expertise in Databricks or Snowflake, Medallion Architecture, cloud data platforms, Generative AI, Agentic AI, and intelligent automation, along with the ability to connect business strategy to technology execution.

Leads Syngenta’s Corporate Function Enterprise Data Hub within the Databricks Data Mesh. Owns data-product strategy, delivery, governance, legacy migration, analytics, machine learning, and AI solutions. Builds and manages a multidisciplinary data team while partnering with corporate functions, shared services, and IT leadership to prioritize demand, deliver measurable value, ensure responsible data use, and manage capacity, suppliers, risks, and investments.

Design, build, and operate production-grade autonomous AI systems and multi-agent workflows. Develop agentic capabilities using OpenAI, Google Gemini, and Perplexity; implement orchestration, reflection, planning, evaluation, observability, and human-in-the-loop controls. Deploy scalable serverless services with Python and AWS, integrate with .NET systems, manage reliability and cost constraints, and create testing frameworks to monitor and improve agent quality.

Develop and grow sales pipelines for complex HPC, AI, hardware, software, and services solutions. The role supports account managers, develops strategic opportunities, builds executive-level customer relationships, drives negotiations and deal closures, grows renewals, manages partner engagement, and maintains market and competitor expertise. Candidates will provide consultative solution selling, create account strategies, forecast revenue in Siebel, and promote integrated technology and services portfolios.

Owns QAD Support’s AI roadmap and governance while coordinating cross-functional initiatives from opportunity identification through implementation, adoption, and optimization. The role manages milestones, dependencies, risks, stakeholders, vendors, success criteria, and measurable business outcomes. It also drives AI enablement, training, communications, continuous improvement, and compliance with security, privacy, legal, and data governance requirements. This is an individual contributor position requiring strong program management and practical AI knowledge, without hands-on AI development.

Lead the architecture and development of scalable batch, streaming, and event-driven data platforms supporting analytics, machine learning, generative AI, and agentic AI. Build governed lakehouse architectures, reusable data products, ingestion and orchestration pipelines, and AI lifecycle capabilities. Drive security, governance, observability, reliability, performance, and cost optimization while translating R&D concepts into production systems. Provide hands-on technical leadership, architecture reviews, mentorship, and cross-functional collaboration.

Lead the architecture and hands-on development of scalable batch, streaming, and event-driven data platforms supporting analytics, machine learning, generative AI, and agentic AI. Build governed lakehouse architectures, reusable data products, ingestion and orchestration pipelines, and ML lifecycle capabilities. Establish standards for security, governance, testing, CI/CD, observability, reliability, and cost optimization while providing technical leadership, mentoring, architecture reviews, and cross-functional collaboration.

Leads the architecture, development, and productionization of AI/ML and GenAI solutions for healthcare operations. Responsibilities include building clinical and claims data pipelines, developing LLM and RAG applications, implementing MLOps and LLMOps, ensuring HIPAA-compliant governance, translating business needs into technical roadmaps, communicating with stakeholders, and mentoring engineers.

Lead Alpaca’s global Trading API customer experience organization, including General and VIP Support. Build a follow-the-sun coverage model, establish service standards and KPIs, improve support through data, AI, automation, and self-service, and develop high-performing teams. Partner with Product, Engineering, Trading, Operations, Compliance, and Sales to resolve recurring issues, improve customer workflows, and support institutional trading customers.