Top Artificial Intelligence Jobs in Pune

Design and implement scalable Generative AI solutions. Collaborate with cross-functional teams to convert business needs into AI solutions. Oversee architecture and evaluate the performance of AI systems on cloud platforms.

Lead technical development of AI solutions for finance automation, mentor teams, integrate AI with enterprise systems, and present to executives.

The Engineering Manager will lead Generative AI teams, focusing on AI solutions for conversations, workflows, voice, support, and platform development, while mentoring engineers and driving performance and innovation.

Lead AI initiatives, craft product vision, manage lifecycle, utilize data for decisions, and foster collaboration across teams.

Design and deploy AI/ML models for financial operations, enhance decision-making, and automate finance/accounting processes in collaboration with global teams.

Lead QA strategy for AI rendering and graphics features, mentor junior members, perform AI model validation, architect automation frameworks, and validate model performance across hardware configurations.

The Senior AI Engineer will design, develop, and maintain AI-driven software applications, manage projects, lead code reviews, and mentor team members while ensuring adherence to best practices in software development.

The Senior AI Engineer will design, develop, test, and maintain software applications, focusing on AI technologies while leading projects and mentoring peers.

The Senior AI Engineer will develop and maintain software solutions in a SaaS environment for life sciences, focusing on AI-powered applications and architectural decisions for data management and analytics.

The AI Tooling Program Associate will lead the development and implementation of AI tools, identify automation opportunities, and support operations within the Databricks Professional Services team.

The Senior AI QA Engineer will oversee test planning, automation framework development, API testing, and ensure high-quality AI solutions. Responsibilities include end-to-end testing across AI/ML systems, collaboration with data and DevOps teams, and mentoring junior QA members.

Build and deploy autonomous AI agents and internal copilots that research, enrich, reason, and execute GTM workflows. Implement RAG with embeddings and vector DBs, integrate agents with HubSpot and internal systems, automate data pipelines and scraping, and own agent systems end-to-end from prototype to production.

Drive data-driven decisions for Generative AI products: prepare datasets and annotation pipelines, evaluate LLM performance, analyze user and workflow data, design A/B tests, monitor cost/latency, and deliver insights to product and engineering to improve adoption and AI quality.

Provide application support and IT operations for AI and data platforms, deploy and monitor GenAI and machine learning models, troubleshoot production issues, collaborate with data scientists, and ensure system reliability and performance.

Manage and optimize data annotation and machine learning workflows, ensuring high-quality data for AI models, while engaging with clients and providing team training.

This role involves preparing proposals, understanding client requirements, assisting in technical solution design, and supporting sales teams in AI/Data projects.

Lead AI growth strategy to drive adoption, engagement, and revenue. Own AI adoption funnels, onboarding, pricing, experiments, analytics, launches, and cross-functional GTM to scale AI products across millions of businesses.

Lead the AI platform strategy and execution: define reusable AI primitives, own model orchestration, data/context pipelines, evaluation, observability, and governance. Enable internal teams to ship AI features faster and safely, measure adoption and reliability, and communicate strategy to stakeholders.

Lead design and delivery of generative AI solutions: translate client needs into architectures, define technologies and NFRs, implement RAG and fine-tuning approaches, run POCs, review designs, and ensure production deployments and MLOps automation.

Translate client use cases into technical designs and architectures for generative AI solutions. Define technologies, NFRs, benchmarks, and design documents. Review and guide implementation for scalability, security, and extensibility, resolve technical issues, and run POCs to validate solutions.

Design and build AI data architecture and APIs with Python/Django; manage SQL, MongoDB and Pinecone vector databases; integrate and optimize AI models, context/prompt builders; collaborate with leadership and engineers; research AI/data engineering advancements.

Lead AI strategy and implementation for content processing at scale: research and prototype LLM and CV solutions, design robust, high-throughput AI services and pipelines, solve production ML challenges (latency, variance, observability), and mentor engineering teams to deliver mission-critical AI features.

As an Applied AI Engineer, you will design, develop, and deploy AI and automation solutions, collaborating with various stakeholders. Responsibilities include building workflows, integrating LLMs, improving solutions, and producing documentation.

As a Senior AI Engineer, you will lead machine learning initiatives from research to production, focusing on insights from unstructured data using Computer Vision and NLP techniques.

The Senior Engineer will develop and deploy machine learning models and workflows, mentor junior engineers, and collaborate with cross-functional teams to improve patient care through innovative solutions.