Top Artificial Intelligence Jobs in Pune

Join Welo Data’s remote Afrikaans contributor network to support AI training projects through text, image, or audio annotation; search, advertising, and chatbot evaluation; prompt creation; and cultural and linguistic quality review. Projects vary from several days to weeks, with compensation and timelines provided before each assignment. This is a talent-community application rather than an active job opening.

Join Welo Data’s Croatian talent network for potential remote AI projects involving text, image, or audio annotation; search, advertising, and chatbot evaluation; prompt creation; and cultural or linguistic accuracy reviews. Projects vary from several days to weeks, with scope, timelines, and compensation provided before participation. This is a talent community registration rather than an active job opening.

Join Welo Data’s remote Yoruba AI contributor network based in Nigeria. Projects may involve annotating text, images, or audio; evaluating search results, advertisements, or chatbot responses; creating prompts for large language models; and reviewing cultural and linguistic accuracy. Project duration, scope, timelines, and compensation vary. This is a talent community opportunity rather than an active job opening.

Join Welo Data’s Russian-language AI contributor network for flexible, remote projects involving text, image, and audio annotation; search, ad, and chatbot evaluation; prompt creation; and cultural and linguistic quality review. This is not an active opening. Contributors are contacted when suitable projects become available and receive project-specific details about tasks, timelines, and compensation.

Join Welo Data’s Basque contributor network for potential remote AI projects involving data annotation, search and chatbot evaluation, prompt creation, and linguistic or cultural quality review. Projects vary from several days to weeks, with scope, timelines, and compensation provided before participation. This is a talent-network opportunity rather than an active job opening.

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

Develop and deploy generative AI, LLM, conversational AI, and autonomous agent applications. Build scalable AI pipelines, fine-tune foundation and deep learning models, integrate agentic workflows, and optimize model performance for production. Collaborate with data engineers on datasets and stay current with advances in GenAI, LLMs, RAG, multimodal learning, and MLOps.

Build and improve scalable business operating systems across recruitment, HR, retail training, ecommerce, marketing, product, and reporting. The role converts founder-led knowledge and manual workflows into trackers, dashboards, SOPs, forms, source-of-truth documents, and AI-assisted processes. Responsibilities include process mapping, workflow testing, document control, access management, issue tracking, meeting-to-action systems, reporting, and user adoption. The role supports business owners but does not make final hiring, legal, strategic, or purchasing decisions.

Develop scalable full-stack web applications and AI-powered solutions using ReactJS, Python, FastAPI, PostgreSQL, APIs, cloud platforms, LLMs, RAG pipelines, and AI agents. Responsibilities include designing frontend and backend systems, optimizing databases, integrating internal and third-party APIs, deploying secure cloud applications, and following testing, code quality, security, and CI/CD best practices.

Design and build production-grade AI applications embedding ML/LLM capabilities. Integrate trained models into APIs, services, UIs, and workflows. Operationalize models (versioning, deployment, monitoring), implement guardrails, ensure security/privacy/compliance, apply bias mitigation and explainability, and mentor team members on AI engineering best practices.

Develop and maintain Python applications, APIs, workers, integrations, and AWS Lambda functions. Manage AWS and Linux environments, infrastructure automation, deployments, CI/CD pipelines, and production support. Improve reliability through monitoring, canary releases, rollback, and recovery practices. Maintain databases, messaging, authentication, security, dependencies, and external integrations. Apply AI-assisted development tools with human review and security validation. The role requires ownership of software delivery from application code through production.

Transcribe unscripted Odia conversational audio verbatim in Odia script for AI speech model training. Responsibilities include preserving fillers and speech patterns, handling Odia-English/Hindi code-switching, tagging audio issues, following formatting guidelines, maintaining 97%+ accuracy, meeting turnaround times, and revising work based on QA feedback.

Lead ML engineering for Operational Intelligence: design and productionize multimodal transformer and agentic systems, build scalable RAG/Graph-RAG and LLMOps/MLOps pipelines, implement inference services and memory/state subsystems, apply traditional and deep learning methods, enforce Responsible AI, and drive research-to-production innovation.

The ServiceNow Solution Architect - AI role involves leading and mentoring a team, designing and deploying AI-driven applications, managing application development, and integrating solutions into ServiceNow modules. Responsibilities include coordinating with stakeholders, troubleshooting, and ensuring adherence to technical standards.

Develop, train, deploy, and maintain machine learning and generative AI solutions, including predictive models, NLP applications, chatbots, LLMs, and agentic AI systems. Design scalable ETL pipelines, optimize databases, process large datasets with Spark and Snowflake, and create business visualizations. The role requires expertise across Python, SQL, cloud AI platforms, machine learning frameworks, databases, and data engineering.

Develop NLP and generative AI models for text classification, sentiment analysis, entity recognition, translation, and semantic analysis. Responsibilities include text preprocessing, feature engineering, model training and evaluation, exploratory data analysis, algorithm optimization, and large-scale data processing. Collaborate with engineering and domain teams to integrate solutions, document methodologies, and apply emerging NLP and machine learning techniques.

Drive outbound sales for Codebase in the US by identifying target SaaS/tech accounts, generating qualified leads, managing lead-nurturing, coordinating discovery meetings, collaborating on proposals and sales collateral, maintaining CRM records, and meeting lead-generation and pipeline targets for software development, IT consulting, and staff augmentation services.

Lead implementation and operations of enterprise observability and AIOps platforms (Elastic stack preferred). Onboard infrastructure, cloud, and applications; build dashboards, alerts, ingestion pipelines; support incident investigation, automation, IaC, upgrades, and runbook documentation to improve reliability and operational readiness.

Build and improve production LLM agents and AI features through evaluation, experimentation, observability, prompting, context engineering, tool use, and workflow design. Develop backend services, APIs, data models, and feedback pipelines that make agent behavior reliable and measurable. Investigate performance issues, run staged rollouts and production replays, define quality standards, and partner with Product, Data Science, and Sales to deliver business outcomes with appropriate privacy, security, and human-oversight safeguards.

Own the AI product roadmap from discovery through launch and iteration. Define model evaluation standards, failure modes, rollback plans, and agent-autonomy boundaries. Prototype and ship production AI features, influence architecture, and apply prompting and context design. Collaborate with Design, Research, Engineering, Sales, Marketing, and Customer Success while tracking quality, latency, cost, adoption, and growth metrics.

Build and optimize high-performance local AI inference software for NVIDIA RTX and DGX GPUs. Develop inference runtimes and execution stacks, optimize models and data pipelines using quantization, pruning, sparsity, and distillation, and perform system-level debugging and performance analysis. Collaborate with software, research, architecture, and product teams to improve scalability, stability, accuracy, and production readiness across language, vision, speech, and diffusion workloads.

Build the company’s central AI intelligence engine by integrating product, CRM, marketing, and support data. Develop scalable APIs, middleware, webhooks, agentic workflows, RAG systems, and LLM-powered applications. Ensure AI outputs are grounded, evaluated, secure, and governed. Collaborate on data pipelines and use SQL and cloud data warehouses to support reliable real-time automation across business functions.

Own product marketing for Cyara’s Agentic AI assurance products, including positioning, messaging, go-to-market launches, competitive strategy, sales enablement, technical content, analyst and press engagement, and pipeline measurement. Partner closely with Product, Sales, Demand Generation, and Customer Success to translate technical capabilities into compelling enterprise-market narratives. The role requires experience in B2B SaaS or AI product marketing, enterprise sales-led motions, technical audiences, and measurable product launches.

Develop, fine-tune, deploy, and optimize AI/ML models and LLM-based RAG solutions using Python and AWS Bedrock. Build REST APIs and microservices with FastAPI or Flask, deploy scalable cloud infrastructure, manage containerized workloads, and develop data pipelines and ETL processes. Work with relational and NoSQL databases, CI/CD, and AWS services while collaborating with technical and product teams and documenting AI workflows.

Design, develop, and deploy production-grade generative AI and agentic systems, including RAG pipelines, autonomous agents, prompt architectures, and enterprise integrations. Build solutions using LangChain, LangGraph, vector databases, cloud services, APIs, and DevOps tooling. Advise stakeholders, document and monitor AI applications, mitigate technical and security risks, modernize RPA assets, and mentor junior developers.