Top Artificial Intelligence Jobs in Pune

The role involves using scientific expertise to evaluate AI-driven life science tools, designing criteria for AI solutions, and ensuring alignment with scientific data and customer needs.

Develop AI workflows and oversee systems that automate complex processes. Mentor junior engineers and collaborate with teams for AI implementation strategies.

Manage AI-driven search optimization by creating AI-ready content briefs, performing technical SEO checks, using analytics tools to surface insights, coordinating cross-functional efforts, and tracking performance to meet marketing KPIs.

Design, build, and scale AI-powered automation workflows using n8n and similar tools. Integrate systems via APIs/webhooks, connect CRMs/marketing/databases/AI models, ensure scalability and reliability, debug and monitor automations, and document solutions while collaborating with business stakeholders.

Design, build, and productionize internal AI-powered applications and workflows. Develop frontends, backend APIs, containerize and deploy via Snowflake SPCS, implement CI/CD, secure access, observability, and integrations with enterprise SaaS.

Build and scale Snowflake-native AI execution layers and orchestrated agent workflows. Implement RAG, embedding pipelines, vector retrieval, context-routing, monitoring, and integrations with enterprise data using SQL, Python, and DBT. Ensure scalable, observable, and governed AI systems across the organization.

Lead design, development, and deployment of scalable ML solutions (recommendations, fraud detection, credit risk) on AWS. Build MLOps infrastructure, monitoring/alerting, support production models (on-call), mentor engineers, and collaborate with product teams to deliver and scale AI-driven features.

Design, build, and own AI-powered full-stack platforms and features from discovery through production. Embed AI into products and workflows, ensure reliability and security, support incidents, and mentor teammates.

Lead design and implementation of generative AI solutions: translate business needs into scalable architectures, define technologies and benchmarks, oversee RAG/prompt engineering, fine-tuning, MLOps, and production deployments, and mentor developers while carrying out POCs and design reviews.

Develop and manage AI-enabled full-stack applications, integrating AI models and APIs, while collaborating with teams and researching new technologies.

The Technical Architect for AI/ML will define architecture for AI systems, mentor teams, and align strategies with business objectives, utilizing cloud-native tools and frameworks.

Develop responsive front-end and robust back-end systems, collaborate on AI integrations, maintain databases, and implement DevOps practices. Emphasize on security and rapid prototyping in a dynamic, startup-like environment.

The AI Platform Engineer will design and optimize AI/ML infrastructure, automate pipelines, and manage Kubernetes for effective machine learning model deployment and collaboration.

The Engineering Manager - AI Growth leads engineering for AI product adoption and growth, managing monetisation tech and collaborating across teams for product success.

SDE3 will build and scale Generative AI products, implement features end-to-end, design architecture, and lead development teams while ensuring high coding standards.

Design, provision, and manage scalable cloud infrastructure and Kubernetes clusters for an Agentic AI platform. Build and operate IaC (Terraform), CI/CD pipelines, observability, autoscaling, and release strategies, and provide operational support for large GPU-backed ML workloads. Improve reliability, deployment velocity, and performance of production systems.

Lead client-specific QA programs for generative AI data: define sampling and audits, run root-cause analyses and CAPAs, plan rater training/certification, maintain performance dashboards, manage escalations, standardize SOPs, pilot automations, coach teams, and ensure data compliance and security.

As a Senior AI Engineer, you will design and implement scalable AI systems, mentor junior engineers, and ensure impactful, ethical AI solutions. Responsibilities include collaborating with stakeholders and fine-tuning generative AI models.

Design, build, and deploy LLM-powered agentic and conversational AI for healthcare, including voice automation (IVR/ASR/TTS), RAG architectures, HIPAA-compliant systems, EHR integrations, developer enablement, and mentoring of junior engineers.

Design, build, and deploy GenAI and agentic AI solutions using LLMs and hyperscaler services. Implement RAG pipelines, fine-tune models, manage backend integrations, ensure scalability, security, and production readiness, and collaborate with cross-functional teams to translate business needs into AI systems.

Design, build, and productionize scalable AI/ML pipelines and models (including LLMs and Generative AI). Deploy and optimize models across cloud and edge, implement MLOps (CI/CD, versioning, monitoring), and collaborate with data scientists and engineering teams to deliver reliable, low-latency AI solutions.

Design, develop, and deploy Generative AI applications on GCP using Vertex AI. Build LLM-powered solutions (chatbots, summarization, document intelligence), implement RAG with vector databases and embeddings, manage token usage and prompt engineering, develop backend services/APIs, fine-tune models, and monitor and scale AI workloads while ensuring performance, reliability, and security.

Design and develop machine learning infrastructure and models to enhance user experience. Collaborate with teams on AI integration and data lifecycle management.

Lead the design and implementation of data infrastructure for analytics and AI; manage ETL solutions and Master Data Management; mentor data engineering team.

Assist in developing AI solutions for financial planning, working on projects involving data structures, algorithms, and integrating AI features into the platform.