Top Artificial Intelligence Jobs in Pune

Develop, train, deploy, and maintain machine learning and AI models for predictive analytics, NLP, conversational AI, and automation. Build scalable solutions using cloud platforms, big data technologies, databases, ETL pipelines, visualization tools, LLMs, and agentic AI systems. Collaborate with a global client and adapt solutions to evolving AI requirements.

Design, build, train, and deploy AI/GenAI models (NLP and computer vision). Move research models into production via APIs/microservices/cloud, optimize performance, handle large datasets, and collaborate with data scientists and engineers to align solutions with business objectives.

Lead AI/ML engineering vision and roadmap for healthcare and life sciences. Oversee multiple client engagements, design and deploy scalable production-grade AI solutions (ML, NLP, deep learning, computer vision, LLMs), ensure regulatory readiness, responsible AI practices, and drive adoption and value realization while managing executive client relationships and expanding AI adoption.

Lead the architecture and development of scalable full-stack Sales AI applications. Integrate generative AI models, retrieval systems, APIs, and agentic workflows into secure production solutions. Guide architecture across front-end, back-end, data, integrations, and cloud infrastructure while improving testing, delivery, observability, reliability, and maintainability. Lead technical decisions, investigate complex issues, mentor engineers, and collaborate with product, AI, data, security, and engineering teams.

Lead enterprise AI and machine learning strategy, architecture, development, and production deployment. Design predictive analytics, generative AI, LLM, RAG, and agent solutions; establish MLOps, governance, monitoring, and responsible AI practices; align initiatives with business value and ROI; communicate with executives and stakeholders; prioritize and deliver AI programs; and mentor data scientists, ML engineers, and analysts.

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.

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.

Lead innovation and process improvements in testing, integrate AI into QA practices, perform automation scripting, and mentor Quality Engineers.

Lead technology vision and architecture for enterprise projects; design cloud-native, data-driven and Generative AI solutions; ensure scalability, security, governance, and engineering excellence; run architecture reviews, POCs, and implementations; evaluate emerging technologies; influence clients via workshops and executive engagements; mentor architects and distributed engineering teams.

Design, develop, and deploy enterprise agentic AI applications using LLMs, RAG, and orchestration frameworks. Build scalable AI services, APIs, reusable components, and integrations with PLM, SAP, and other enterprise systems. Translate business needs into AI-driven workflow solutions, improve quality through prompt engineering and evaluation, and support the full software development lifecycle through production. Collaborate with business, IT, and engineering teams to deliver secure, reliable, cloud-native AI applications.

Design, develop, deploy, and operate secure full-stack applications with AI, machine learning, and large language models embedded as core capabilities. Build front ends, APIs, backend services, and data pipelines primarily in Python. Deliver cloud-native solutions across AWS, Azure, and hybrid environments while implementing security, scalability, testing, CI/CD, monitoring, and observability. Collaborate with business, technology, security, and operations teams to deliver enterprise-ready applications.

Designs and scales enterprise AI solutions using Microsoft Copilot, Azure AI Foundry, and Anthropic Claude tools. Builds agentic workflows, intelligent agents, enterprise integrations, document-processing solutions, and automated decision workflows. Develops reusable AI frameworks, supports CI/CD testing and deployment, aligns solutions with Azure architecture, and collaborates with global teams to improve productivity and automate firm workflows.

Build and deliver production-grade AI-native and cloud-native solutions, including backend microservices, API integrations, GenAI features, agentic workflows, and intelligent automation. Responsibilities include testing, code reviews, troubleshooting, technical debt reduction, cloud deployment, client technical consulting, proof-of-concept support, mentoring junior engineers, and contributing to engineering frameworks and architecture discussions.

Coordinates the AI Accountability Network’s newsletters, training programs, grants, fellowships, payments, events, communications, and community initiatives. Manages editorial workflows, contractors, applications, logistics, websites, listservs, dashboards, metrics, and stakeholder communications. Supports grant administration, funder reporting, travel, curriculum development, and website publishing while improving processes and tracking program impact.

Build and refine AI-powered prototypes for supply chain and manufacturing operations. Engage with factory and warehouse teams, translate operational needs into software requirements, develop Python-based RAG and agentic systems, integrate APIs and data sources, and iterate prototypes based on frontline feedback. The role involves SQL and document data preparation, agile collaboration, occasional manufacturing-site travel, and progressive adoption of enterprise AI frameworks and production engineering practices.

Lead post-launch operations for Sales AI applications, ensuring availability, performance, security, governance, support readiness, and continuous improvement. Establish incident, problem, change, release, and lifecycle processes; monitor service and adoption metrics; develop automation, observability, self-service capabilities, and production-readiness practices. Partner with Sales, Product, Engineering, Data, Security, and IT teams to improve user experience, platform reliability, adoption, and business value.

Design, build, and evolve secure, scalable full-stack sales applications incorporating generative AI, intelligent workflows, APIs, data services, and cloud infrastructure. Lead architecture and technical design decisions, integrate AI models and retrieval systems, improve testing, CI/CD, observability, and production reliability, and mentor engineers. Collaborate with Product, AI/ML, Data, Security, Solution Architecture, and Engineering teams to deliver enterprise applications globally.

Design, build, and scale production AI agents across EveryWatch. Own the agent roadmap, architecture, shared tools, memory, state, observability, evaluation, and cost controls. Harden WatchChat, develop RAG and tool-calling systems, establish measurable quality standards, and ship solutions directly in Python. Collaborate with sales, product, data, and engineering teams to identify high-impact use cases and take agent concepts from discovery through production.

Join Welo Data’s Canadian French contributor network for potential remote AI projects. Depending on project needs, contributors may annotate text, images, or audio; evaluate search results, advertisements, and chatbot responses; create prompts for large language models; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with compensation and details provided before each assignment.

Join Welo Data’s French AI Trainers Network as a flexible contributor supporting annotation, evaluation, and prompt creation projects. Contributors may label text, images, or audio; assess search results, advertisements, and chatbot responses; create prompts for large language models; and ensure cultural and linguistic accuracy. This is a talent community rather than an active opening, with project details and compensation provided when assignments become available.

Designs and implements end-to-end generative AI and LLM solutions, researches emerging AI techniques, develops supporting libraries and frameworks, and participates in software delivery from design through maintenance. The role applies NLP, computer vision, audio, and multimodal AI methods; uses cloud and open-source platforms; contributes to code reviews and roadmaps; and collaborates with cross-functional teams.

Design, build, test, deploy, and monitor production automations that integrate Python, REST APIs, cloud data stores, and Large Language Models. Implement prompt engineering, structured output parsing, validation, and orchestration; create lightweight React/FastAPI interfaces when needed; follow agile practices, maintain shared libraries and repos, and hand off documented automations to operations teams.

Lead the end-to-end product experience across acquisition, onboarding, avatar selection, chat, media generation, and retention. Drive conversion, retention, and LTV through hypotheses, prioritization, A/B testing, and data-informed design. Establish the product’s design direction and design system, lead a multidisciplinary design team, and partner with product, analytics, marketing, engineering, research, and support.

Design and implement scalable end-to-end AI/ML solutions and data pipelines; define secure cloud and on-prem architectures; lead MLOps, CI/CD, model lifecycle management, data governance, and cross-functional collaboration to drive AI initiatives in manufacturing.

Develop automation and AI solutions supporting pharmaceutical clinical trials. Interpret requirements, identify workflow improvements, build proof-of-concept tools using R, Shiny, and LLM-supported coding, and mature successful prototypes into validated, documented, reusable solutions. Collaborate with statisticians, programmers, data scientists, and AI leads while ensuring testing, traceability, audit trails, version control, and compliance with regulated clinical development expectations.