Top Artificial Intelligence Jobs in Hyderabad

Manage enterprise client relationships in Hyderabad, drive renewals and upsells, support technical recruiting squads, resolve client issues, and represent Xenon7 to grow revenue and ensure client satisfaction.

Builds corporate hiring partnerships with technology companies, pitches talent acquisition proposals, and organizes recruitment drives. Manages end-to-end student placement processes, tracks placement KPIs, and collaborates with academic and operations teams to improve interview readiness. Provides guidance on resumes, LinkedIn profiles, mock interviews, and placement etiquette while monitoring technology-sector hiring trends and in-demand skills.

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.

Work on AI/ML/Deep Learning models for new use cases, gain exposure to real-world industry data, produce written artifacts (articles), participate in guided knowledge-transfer sessions, and potentially earn recommendation or a pre-placement offer based on performance.

Lead analytics and AI consulting engagements for CPG and retail clients. Structure complex business problems, design solutions, develop proofs of concept, advise executive stakeholders, manage client relationships, and deliver analytics transformation programs. The role also includes creating data assets, driving market adoption, recruiting and developing talent, and building analytics or AI communities of practice.

Design, build, and deploy AI/ML and Generative AI solutions for enterprise use cases. Build and maintain end-to-end ML pipelines, deploy models to production, and implement monitoring, observability, and model-drift detection. Work with APIs, vector databases, embeddings, and RAG-based applications, and collaborate with cross-functional teams to optimize scalable AI deployments.

Validate AI voice bot functionality by creating/executing test cases from business documents, running functional/regression/integration/API tests, verifying conversational flows and intent recognition, supporting automation efforts, managing defects, and assisting production validation and release activities.

Lead design, build, deploy, and monitor enterprise-scale AI/ML and Generative AI systems. Own end-to-end ML pipelines, scalable inference, vector search and RAG applications, MLOps, observability, governance, CI/CD, model lifecycle management, and production support. Mentor teams, collaborate with stakeholders, and optimize AI systems for scalability, latency, reliability, and cost.

Design, build, and deploy production-grade AI applications integrated with LLMs to automate and optimize investment management workflows. Evaluate generative models, implement RAG and vector DB solutions, create automated workflows, collaborate with cross-functional teams, ensure security/compliance, and contribute to architecture, code reviews, and documentation.

Build and ship production-grade full-stack applications for agentic AI use cases. Own user experience, application architecture, enterprise security, permissions, agent safeguards, auditability, deployment, scalability, observability, and marketplace packaging. Translate complex AI and graph capabilities into intuitive investigation workflows, collaborate with customers and cross-functional teams, and create reusable, configurable products deployable across enterprise customers and cloud environments.

Build agentic AI and GraphRAG systems, MCP tools, orchestration workflows, LLM integrations, graph query pipelines, vector search components, and developer-facing SDKs. Integrate TigerGraph with vector indexes and external LLMs, create reusable agent modules and prompts, benchmark groundedness and performance, write tests, document tooling, and collaborate with platform, research, and product teams.

Build and deploy production-grade AI/ML systems spanning LLMs, RAG, agentic AI, computer vision, edge AI, and autonomous robotics. Responsibilities include model fine-tuning, distributed system architecture, GPU optimization, simulation-based robotics development using MuJoCo and Isaac, autonomous driving simulation, and end-to-end algorithm deployment. The role requires translating business problems into scalable AI products and contributing to commercialization and product-market fit.

Design, develop, and deploy generative AI and agentic AI solutions on SAP BTP. Build side-by-side CAP and RAP extensions, Fiori/UI5 applications, Joule copilots, RAG solutions, knowledge graphs, and API integrations across SAP and non-SAP systems. Lead BTP service design, manage customer expectations, mentor junior developers, conduct code reviews, and promote clean-core practices. Global travel and flexible working hours may be required.

Design and build production AI systems: agentic workflows, multi-agent architectures, RAG, tool-using AI, evaluation and observability pipelines, scalable async execution, failure handling, and standards. Own deployment, monitoring, and mentoring for AI features.

Engage with clients to identify AI integration opportunities using Beacon.li's platform, translate requirements into product features, drive roadmap prioritization, collaborate with engineering/design/data science, deliver demos/workshops, and monitor post-deployment performance to ensure client value.

Manage full sales cycle for Oorwin AI in the US region: prospecting, demos, negotiating contracts, closing deals, and account expansion. Maintain CRM records, collaborate with marketing, product, and customer success, and consistently meet monthly quotas and performance targets.

Develop and integrate AI-driven features into web applications. Build responsive, interactive UI components with JavaScript, HTML, and CSS. Work with AI/ML models and APIs, optimize performance for scalability, collaborate with product/design/engineering teams, write clean maintainable code, and communicate progress and challenges to stakeholders and clients.

Design and operate governed, AI-ready data products, semantic layers, and natural-language analytics experiences on Snowflake and/or Databricks. Build ETL/ELT pipelines with dbt, Airflow, Snowpark, and PySpark; deploy Cortex or Genie capabilities; develop RAG applications; implement governance, security, lineage, and metadata controls; optimize platform and LLM costs; and partner with Finance stakeholders on trusted data products.

Lead end-to-end enterprise AI engagements for supply chain clients, from presales discovery and solution design through development, deployment, go-live, and post-deployment success. Advise executives, run workshops, scope use cases, design AI architectures, present proposals and demos, collaborate with engineering and data science teams, debug production systems, mentor teams, and develop reusable AI thought leadership and accelerators.

Design, develop, and deploy enterprise Generative AI applications using LLMs, RAG, and agentic AI frameworks. Build conversational assistants, chatbots, document intelligence platforms, copilots, autonomous agents, vector search pipelines, and scalable Python/FastAPI services. Deploy solutions across Azure, AWS, or GCP using Docker, Kubernetes, and CI/CD. Monitor production AI performance, collaborate with architects and product teams, participate in Agile ceremonies, and mentor junior engineers.

Build and scale enterprise GenAI-enabled web applications using Node.js, Java, and Python. Develop APIs, microservices, responsive interfaces, LAG pipelines, vector database integrations, LLM features, agentic workflows, and MCP integrations. Deploy cloud-based applications using Docker, Kubernetes, and CI/CD pipelines. Collaborate with product, AI, and UX teams, mentor junior developers, participate in Agile ceremonies, and lead delivery outcomes.

Develops full-stack AI and machine learning applications using web technologies, statistical modeling, deep learning, databases, cloud platforms, and APIs. Designs algorithms, translates technical specifications into code, performs testing and debugging, documents solutions, and improves product and system performance. Works independently on complex initiatives, collaborates with stakeholders, mentors colleagues, and may manage projects or review others’ work.

Leads a hands-on India engineering team delivering AI-enabled business process solutions across the Microsoft stack. Sets architecture and engineering standards, designs and ships agentic AI solutions, coaches engineers, improves delivery throughput, builds reusable accelerators, supports client engagements and presales, and helps shift consulting work toward fixed-fee and managed-services offerings.

Design, develop, and deploy generative AI and NLP solutions using Python and related libraries. Integrate models with React front ends, use LangChain/LangGraph, AWD Bedrock and Azure Foundry for deployment, optimize model performance, handle large datasets, implement RAG and vector search, mentor junior engineers, and maintain documentation and best practices.