Top Artificial Intelligence Jobs in Pune

Leads enterprise platform architecture across hybrid and cloud infrastructure, AI platforms, security, compliance, modernization, and sustainability initiatives. Defines target-state architectures, reference patterns, and roadmaps; drives automation and platform simplification; partners with security and technology teams; manages budgets and multiple teams; mentors architects and engineers; and influences executive technology strategy.

Provides systems safety engineering for aircraft turbine engines and auxiliary power systems. Conducts hazard and safety analyses, develops safety requirements and documentation, supports FAA and EASA compliance, performs reliability analysis, and reviews designs. Collaborates across engineering teams on risk mitigation, corrective actions, and aftermarket safety metrics. The role also supports process standardization, automation, AI application, technical coaching, innovation, and strategies to reduce certification cycle time and cost.

Designs data-driven digital products, prototypes, dashboards, applications, chatbots, and interfaces using human-centered design, Lean UX, and Design Thinking. Leads workshops, develops personas and user journeys, creates visual and interaction designs across web, mobile, and IoT, and collaborates with product, technology, marketing, and regional teams. Conducts iterative user testing, contributes to design systems, and shapes AI- and ML-enabled experiences while balancing usability, business goals, regulatory compliance, and global user needs.

Own product strategy and end-to-end feature development for Mastercard’s Test & Learn business experimentation platform. Conduct user and market research, define roadmaps, write requirements, collaborate with engineering and UX, measure product impact, and identify opportunities to enhance experimentation and optimization through AI.

Own the strategy, roadmap, backlog, and success metrics for an enterprise AI agent platform serving financial operations teams. Lead customer discovery, product requirements, AI capability prioritization, compliance and governance integration, platform investment decisions, stakeholder alignment, and commercialization. Translate operational workflows and AI capabilities into measurable, production-ready products while balancing customer value, technical feasibility, cost, latency, data governance, and regulatory requirements.

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.

Leads a Pune-based software engineering team building and operating shared data platform capabilities. Responsibilities include managing and developing engineers, shaping technical direction with product management, improving production data pipelines, migrating legacy workloads to cloud platforms, automating customer onboarding, enforcing data quality and security standards, managing incidents, coordinating vendors, and communicating progress to senior leaders. The role is 40–50% hands-on and requires recent Databricks and AWS experience.

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.

Designs, builds, and operates production-grade agentic AI systems using Python, LLM orchestration, tool calling, retrieval, memory, multimodal models, and LLMOps. The role develops scalable backend services, observability, RAG and Graph-RAG systems, model evaluation, and responsible AI safeguards. It also mentors engineers, establishes GenAI engineering standards, and productionizes emerging AI capabilities across enterprise applications.

Build and operate production-grade agentic AI systems, including tool-calling layers, Python backend services, multi-agent orchestration, memory, retrieval, observability, LLMOps, and multimodal pipelines. Develop RAG and Graph-RAG systems, fine-tune and evaluate foundation models, implement responsible AI controls, and mentor engineers. The role requires strong backend engineering, API integration, cloud, distributed systems, and production LLM application experience.

Leads client-facing AI and digital transformation engagements from strategy through implementation and adoption. Manages cross-functional teams building enterprise Generative AI, Agentic AI, machine learning, and analytics solutions. Advises senior stakeholders, develops client relationships, supports business development, manages budgets and vendors, ensures engineering and architecture quality, hires and mentors talent, and drives AI scaling initiatives across consulting engagements.

Develop secure, scalable full-stack applications using Java, Spring, REST APIs, frontend frameworks, cloud platforms, microservices, and databases. Collaborate with product owners and technical teams on architecture, Agile planning, coding, testing, deployment, and support. Work with Databricks, Spark, ETL pipelines, AI/ML services, containerization, CI/CD, and modern development tools while contributing to application design and user experience.

Designs, builds, and operates production-grade agentic AI systems and generative AI applications. Responsibilities include tool-calling infrastructure, Python backend services, agent orchestration, memory and retrieval systems, observability, LLMOps/MLOps pipelines, multimodal AI, model adaptation, responsible AI, and production evaluation. The role also mentors engineers and establishes engineering standards for enterprise generative AI development.

Lead architecture and technical strategy for an enterprise generative AI platform supporting agentic systems, tool calling, multi-agent orchestration, memory, retrieval, inference, LLMOps, observability, security, and responsible AI. Define reusable standards, evaluate models and platforms, guide architecture decisions, collaborate with engineering, product, security, and data teams, mentor technical leaders, and build prototypes for high-scale, reliable production systems.

Develop secure, scalable full-stack applications using Java, Spring, REST APIs, relational databases, and modern frontend frameworks. Responsibilities include designing software, building responsive interfaces, participating in architecture and Agile ceremonies, supporting testing, and collaborating with product and technical teams. The role also involves cloud deployment, microservices, CI/CD, containerization, Databricks and big data workflows, and integrating AI/ML services and development tools.

Build, test, release, monitor, and support platform services, APIs, connectors, and automation for customer data exchange and pipeline orchestration. Deliver components independently, write tests and documentation, replace manual processes with code, investigate production issues, and contribute to design, estimation, code reviews, and root cause analysis. The role uses cloud technologies, data platforms, secure coding practices, and CI/CD.

Develop and scale globally relevant data analytics and agentic AI products for enterprise and credit risk customers. Partner with analytics, machine learning, engineering, operations, legal, customer experience, commercial, and product teams. Gather customer feedback, analyze data, define product direction, create requirements and user stories, communicate product updates and risks, and support go-to-market initiatives throughout the product lifecycle.

Develop and maintain scalable full-stack enterprise applications using Java, Spring, React or Angular, microservices, and cloud-native AWS technologies. Responsibilities include requirements gathering, architecture and design, coding, testing, monitoring, performance engineering, compliance, and production support. The role also involves database development, CI/CD, DevOps, containerization, data engineering, and integration of AI/ML services. Candidates collaborate with business partners and technical teams while mentoring developers and ensuring secure, resilient, high-quality delivery.

Leads AI-enabled analytics engineering and commercial analytics workstreams for life-sciences and other clients. Builds Databricks insight pipelines, agentic LLM workflows, conversational analytics, evaluation harnesses, governed data products, and production-quality software. Structures ambiguous business questions, applies advanced analytical methods, integrates complex data sources, and translates findings into decisions and client-ready deliverables. Manages and coaches teams of 2–4, oversees quality and delivery risks, supports client stakeholders, and contributes to proposals and capability development.

Build and maintain Databricks analytics pipelines, LLM agent tools, evaluation harnesses, data guardrails, and tests while delivering commercial analytics for client teams. Analyze varied healthcare, life sciences, CRM, financial, and operational datasets using segmentation, forecasting, cohorts, and test-and-control methods. Present insights clearly, manage modules end to end, guide Associates, support SIT/UAT, and collaborate directly with clients.

Manage full-cycle technical recruiting for Engineering, Product, Security, UX, and R&D roles. Partner with hiring leaders on role definition, structured interviews, sourcing strategy, market research, candidate assessment, pipeline management, and offer negotiations. Build diverse talent pipelines, use recruiting data to guide decisions, maintain accurate Greenhouse documentation, deliver an inclusive candidate experience, and improve recruiting practices through responsible AI use and shared resources.

Technical Product Owner responsible for connecting customer needs with engineering capabilities, managing and prioritizing product backlogs, defining user stories and acceptance criteria, supporting roadmaps and estimation, coordinating dependencies, facilitating Agile ceremonies, accepting completed work, and applying feedback and delivery metrics to future releases.

Leads data science, AI, and machine learning capabilities across enterprise platforms. Develops cloud-based content classification systems, governs content lifecycle and metadata enrichment, embeds AI intelligence into product roadmaps, and creates agentic AI frameworks. Partners with product, engineering, and domain teams to align technical execution with business goals while mentoring operations and enablement teams.

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.