Top Artificial Intelligence Jobs in Hyderabad

Lead product quality across the SDLC by reducing defects, driving root-cause analysis, defining automation strategy and quality gates, building automated test frameworks (Playwright, API, integration, performance), integrating tests into CI/CD, enforcing release readiness, and using Claude Code and other AI tools to analyze issues and improve engineering outcomes.

Own and design the shared AI system architecture for production GenAI products: retrieval/RAG, agentic workflows, model selection/fallbacks, evaluation/regression testing, and production readiness. Build reusable patterns, mentor engineers, and ensure reliability, cost-awareness, and safe failure modes across products.

Own product lines of agent-based AI systems: write machine-readable specs, design prompt/tool/schema behavior, build HTML prototypes with live model calls, run evaluations, choose model tradeoffs, instrument usage/quality/cost, and work closely with engineering, GenAI, design, and enterprise customers to ship measured outcomes.

Design, build, and deploy AI agents and machine learning models; integrate ML/NLP into full-stack applications; develop APIs and data pipelines; deploy and manage services on AWS; collaborate with frontend/backend teams and maintain CI/CD, databases, and production monitoring.

Develop and optimize AI models for efficient inference on custom silicon. Build fine-tuning pipelines, implement acceleration techniques (quantization, sparsity, distillation), create benchmarking and profiling tools, and collaborate with hardware, compiler, and quantization teams to translate algorithmic improvements into real hardware gains.

Lead enterprise clients to improve AI search visibility through analysis, custom GEO playbooks, C-suite presentations, prompt testing, monthly reporting, and cross-functional product collaboration to drive platform adoption and measurable visibility gains.

Work across growth, funnel analytics, user research, product QA, competitive market analysis, and ad hoc operational tasks. Own outbound/paid campaigns end-to-end, identify funnel drop-offs, run user interviews, test product releases, and drive fixes and launches.

Design, build and deploy AI agents, automation workflows and system integrations using Azure AI, OpenAI and Microsoft Power Platform. Automate business processes, create dashboards and data pipelines, integrate Microsoft Business Central/SharePoint/365 with third-party systems, and lead projects from requirements through deployment and user training.

Build data integrations and tooling that combine CRM, product telemetry, support, billing, and other signals to provide GTM teams with context, automation, and real-time augmentation. Enable account prioritization, allocation, and funnel conversion tracking to increase revenue and save time across sales, marketing, growth, and customer success.

Build and scale an AI-powered stock research platform: design data pipelines ingesting SEC and market data, develop backend APIs, implement semantic search with embeddings and vector DBs, improve production reliability and performance, and own features end-to-end from design through deployment and monitoring.

Work on a TypeScript full‑stack product (Next.js/React) with an AI pipeline using Anthropic Claude, real‑time collaboration (Liveblocks/Yjs/Tiptap), and Cloud Run background jobs. Build UI features, improve AI agent prompts/schemas/output rendering, write and maintain unit tests, read docs to unblock yourself, and grow to own product slices.

Build intuitive, high-performance React interfaces for AI-powered insurance workflows. Collaborate with design and backend teams, create reusable UI components and design systems, integrate frontend features with AI services (document review, entity extraction, quote comparison), rapidly prototype, and iterate based on user feedback.

Build and productionize AI features across frontend and backend: document review, entity extraction, quote comparison, embeddings and LLM integrations, prototype from customer feedback, and create scalable reusable systems for insurance workflows.

Design, develop, fine-tune, and productionize ML and Generative AI/LLM solutions. Build end-to-end ML pipelines (data ingestion, modeling, deployment, monitoring), implement RAG and vector DB architectures, apply prompt/context engineering and MLOps best practices, and collaborate with cross-functional teams to optimize models for performance, latency, scalability, and cost.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Develop and execute organic marketing strategies to drive lead generation. Manage SEO, content-driven email campaigns, and webinars. Analyze campaign performance and optimize for improvements. Support revenue operations (RevOps) to align marketing with sales.

Design, develop, and deploy advanced AI/ML models; manage large-scale data pipelines; build conversational and generative AI solutions; collaborate with stakeholders; monitor and optimize production models; document workflows and implement industry best practices.

The Content Writer/Ai Editor will create high-quality content while leveraging AI tools for optimization, managing multiple projects, and adhering to brand guidelines.

Design and implement AI-enabled ServiceNow solutions across ITSM, FSM, and ITAM. Provide technical leadership and ensure best practices in AI capabilities, facilitate stakeholder workshops, and guide project teams.

The AI Engineer will develop and deploy AI/ML models and solutions, collaborate with teams, and ensure production readiness while addressing complex business problems.

Build and maintain LLM-powered applications and MCP servers, implement tool/function calling and prompt pipelines, rapidly prototype new AI tools, track AI tool releases, and deliver weekly intelligence reports with demos and recommended integrations.

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 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.