Top Artificial Intelligence Jobs in Pune

Lead site reliability and production engineering for a globally deployed payment platform. Define reliability strategy, improve availability and observability, conduct capacity planning, automate operations, support incident and problem management, perform root-cause analysis, and provide on-call support. Partner with development and testing teams on production readiness, deployments, resiliency, and performance. Mentor engineers, manage stakeholders and vendors, improve ITSM processes, and advance AI-driven operational efficiency and risk detection.

Lead the implementation and continuous improvement of an enterprise Project Risk Management operating model and Archer platform. Translate governance, compliance, contractual, and risk requirements into workflows, controls, decision trees, and assessment processes. Drive stakeholder adoption, standardization, governance forums, platform enhancements, and cross-functional collaboration across Legal, Privacy, Security, Compliance, Quality, and Risk teams. Support AI-enabled risk indicators, automation, evidence collection, and decision-support capabilities.

Lead the end-to-end design of Discover, Circle’s AI-driven two-sided marketplace. Set product and design direction, solve discovery and trust challenges for creators and consumers, prototype rapidly with AI-assisted tools, collaborate with engineers through launch, and mentor designers. The role requires deep consumer marketplace experience, ownership of complex multi-user flows, shipped zero-to-one work, and the ability to operate independently in ambiguity.

Provides technical leadership for FAA and EASA certification and qualification of aircraft turbine engines and auxiliary power units. Prepares and reviews certification and qualification plans, reports, and related artifacts; coordinates with aviation regulators; standardizes and improves certification processes; and applies automation and artificial intelligence to reduce cycle time and cost. Supports systems engineering activities, talent development, technical coaching, and innovation across Honeywell Aerospace programs.

Leads the architecture, development, deployment, and lifecycle management of enterprise AI, machine learning, generative AI, and agentic AI platforms. Establishes AI engineering standards, MLOps and AgenticOps practices, cloud-native architectures, governance, security, and operational excellence. Partners with architecture, security, product, data science, and infrastructure teams to define roadmaps, evaluates emerging technologies, mentors engineers, and drives production adoption of scalable AI capabilities.

Lead the design and development of scalable cloud-native data platforms and batch or real-time pipelines supporting AI, machine learning, and Generative AI. Build infrastructure for feature engineering, model training, inference, LLMs, RAG, embeddings, and vector databases. Ensure data quality, governance, security, observability, reliability, and cost efficiency while enabling MLOps and AgenticOps capabilities across enterprise AI initiatives.

Design, build, deploy, and operate enterprise AI, machine learning, generative AI, and agentic AI systems. Productionize prototypes into scalable platforms, develop end-to-end ML pipelines and LLM applications using RAG and vector databases, and implement MLOps and AgenticOps practices. Architect cloud-native services with Kubernetes and microservices, optimize reliability and performance, and ensure security, privacy, compliance, governance, and responsible AI standards. Collaborate with technical and business stakeholders on reusable AI capabilities and strategic roadmaps.

Define and own end-to-end product architecture for Mastercard's switching and network services. Collaborate with product and engineering teams to design scalable, resilient, secure systems, apply AI/LLM capabilities, produce architecture artifacts, validate technical feasibility, and guide implementation while engaging stakeholders across regions and customers.

Design, build, and maintain scalable data pipelines and cloud data platforms to support AI/ML workloads. Deploy, monitor, and operationalize machine learning models, implement CI/CD and MLOps, ensure data quality, scalability, and governance while collaborating with data scientists, engineers, and stakeholders.

Own the strategy and roadmap for generative AI and agentic capabilities within Payment Reconciliation Intelligence. Lead product discovery, user research, experimentation, PRDs, backlog prioritization, technical requirements, and release planning. Collaborate with engineering, data science, platform, and customer experience teams to deliver safe, observable, customer-focused products. Support go-to-market enablement, documentation, training, stakeholder alignment, and mentoring of junior product managers.

Design, build, deploy, and monitor production-grade agentic AI solutions on Google Cloud using Google ADK and Vertex AI. Develop MCP servers, model and tool integrations, enterprise automations, and responsible AI safeguards. Work directly with business teams to identify problems, rapidly prototype solutions, measure business impact, and iterate in production. Collaborate with QA, cloud engineering, security, legal, and data governance teams while documenting solutions, creating runbooks, and enabling business owners.

Designs and develops AI and machine learning models, including NLP and computer vision systems, and deploys them into scalable production applications through APIs, microservices, and cloud platforms. Responsibilities include data preparation, model training, testing, debugging, performance optimization, and collaboration with data scientists and software engineers. The role requires strong programming, ML framework, cloud, SQL, and algorithm development skills.

The Lead AI Scientist will design and optimize LLM architectures, fine-tune models, develop AI systems, mentor junior members, and collaborate with clients and teams on AI solutions.

Lead global hiring and onboarding operations, including background screening, vendor management, regulatory compliance, employee orientation, documentation, HR data maintenance, and colleague support. Use Workday, ServiceNow, analytics, and AI tools to improve onboarding experiences, simplify processes, manage transformations, and advise stakeholders while strengthening controls and ensuring policy compliance.

Develop simulation-driven product designs using DfAM, topology optimization, CFD, heat-transfer analysis, and finite element methods. Optimize designs for additive manufacturing printability, investigate failure modes, validate models against experimental data, and refine simulation methods. Collaborate with product development and business teams on requirements and specifications, then communicate findings through technical reports and presentations.

Analyze complex financial, project, and portfolio datasets; investigate data quality, lineage, and control issues; build data pipelines; apply advanced analytics and AI; create dashboards and automate reporting. Translate findings into actionable recommendations, support forecasts and demand tracking, maintain governance artifacts, resolve queries, and partner with stakeholders across the organization. The role also involves risk management, control strengthening, and influencing operational improvements.

Perform incoming material receipt, documentation review, and release under cGMP/GxP requirements. Maintain quality records and data integrity using Veeva, SAP, GLIMS, LIMS, and electronic QMS platforms. Support investigations, CAPA, audits, inspection readiness, PQRs, supplier collaboration, and process improvements. Apply automation and AI tools to improve quality operations while mentoring junior colleagues and coordinating with global stakeholders.

Leads technology quality and compliance for enterprise ERP and WMS systems across their full lifecycle. Manages computerized system validation, quality strategies, audits, inspections, CAPA, risk management, validation documentation, project portfolios, budgets, and global teams. Partners with stakeholders to embed preventive controls, ensure regulatory compliance, approve production releases, and drive continuous improvement. Requires expertise in pharmaceutical or medical device CSV, quality systems, global program leadership, and regulations including cGMP and 21 CFR requirements.

Designs and scales AI-powered go-to-market workflows across marketing, sales, customer success, and revenue operations. Responsibilities include building AI copilots and agents, automating lead-to-renewal processes, integrating Salesforce and other GTM tools, operationalizing LLMs, improving CRM data quality, enabling predictive analytics, and delivering recommendations that improve pipeline conversion, forecasting, seller productivity, retention, and expansion.

Secure identities for AI systems by discovering and inventorying AI/non-human identities, enforcing least-privilege lifecycle controls, implementing access policies, monitoring identity behavior, performing assessments and remediation, supporting governance and compliance, producing metrics and runbooks, and advising engineering teams on AI identity security best practices.

Design, develop, and optimize secure, scalable cloud data platforms and ETL/ELT pipelines across AWS, Azure, and GCP. Write Python and SQL, implement data governance and quality controls, optimize performance and costs, and support analytics and BI layers. Contribute to architecture, pre-sales demos, proofs of concept, and client discussions. Mentor junior developers, conduct code reviews, and collaborate with architects, QA, DevOps, and business stakeholders.

Own enterprise pipeline generation for strategic accounts across BFSI, manufacturing, and government. Design account-based outbound campaigns, conduct consultative discovery with C-level buyers, maintain CRM data quality, track conversion metrics, and partner with Account Executives on account planning. Mentor junior BDRs, support playbook development, and engage prospects around Rubiscape’s unified data, BI, ML, and AI platform.

Design and develop scalable, skill-aligned online learning experiences focused on technical and AI topics. Partner with subject-matter experts, APAC partners, and vendors to create outcomes, architectures, storyboards, learning journeys, assessments, and production standards. Provide quality assurance and actionable feedback, build reusable design systems, repackage learning assets, and contribute to AI-assisted design tools, agentic workflows, analytics, experimentation, and continuous improvement.

Build and deploy AI-powered customer solutions across discovery, prototyping, and production. Responsibilities include designing secure multi-tenant and hybrid architectures, integrating identity and access management, encryption, networking, data engineering, CI/CD, observability, and production support. The role partners directly with enterprise customers, executive sponsors, and cross-functional teams to solve ambiguous problems, demonstrate prototypes, and standardize successful solutions. Regular domestic and occasional international travel is required.

Develop and implement digital twin architectures for patient modeling, research AI innovations, mentor junior team members, and contribute to client solutions.