Top Artificial Intelligence Jobs in Hyderabad

Build and deploy production-grade machine learning, deep learning, and Generative AI solutions. Responsibilities include developing models, RAG pipelines, AI agents, prompt engineering, data and training pipelines, scalable APIs, and MLOps practices. The role evaluates and optimizes model performance, cost, and latency while collaborating with product, data, and engineering teams to move AI capabilities from proof of concept to production.

Own product marketing, content strategy, brand storytelling, positioning, and organic growth for an early-stage AI and life sciences company. Develop narratives, campaigns, launch materials, customer stories, visual content, and distribution strategies. Collaborate with founders, product, engineering, design, sales, and customer-facing teams to translate complex technology into compelling market-facing content and drive brand awareness, qualified inbound interest, and pipeline influence.

Develop and maintain custom ERPNext and Frappe applications, including DocTypes, workflows, reports, dashboards, print formats, integrations, and APIs. Diagnose bugs, improve performance, maintain existing customizations, and manage deployments through Git and staging environments. Use and review AI-generated code for implementation, debugging, testing, refactoring, security, and maintainability. Communicate progress and clarify requirements while supporting ERPNext workflows and related infrastructure.

Design and operate a production-grade multi-agent AI platform using Gemini models, Google ADK, Pub/Sub, Redis, Firestore, Neo4j, and evaluation tools. Build orchestration, multimodal integrations, RAG, adaptive learning loops, artifact generation, guardrails, and performance optimization. Collaborate across backend, frontend, prompt engineering, and product teams while scaling AI tutoring capabilities and monitoring quality, latency, safety, and model drift.

Lead the end-to-end architecture and delivery of production multi-agent AI systems for automotive dealerships. Build orchestrated voice and chat agents, internal business workflows, MCP tools, RAG systems, guardrails, evaluation frameworks, observability, and LLMOps practices. Ensure reliable tool use, secure human approvals, low-latency realtime experiences, accurate live-data responses, and continuous improvement. Partner with product and platform teams, mentor engineers, and communicate AI risks and system performance to leadership.

Designs, builds, deploys, and supports secure generative AI solutions on Azure. Responsibilities include developing Azure OpenAI and RAG applications, agentic workflows, secure APIs and microservices, cloud deployments, responsible AI controls, monitoring, and enterprise security integrations. The role requires strong Python and Azure expertise, experience with AI search and retrieval, agent frameworks, API development, cloud infrastructure, observability, and secure tool integration.

Architect and deploy scalable generative AI and autonomous agent solutions on AWS, specializing in AWS Bedrock, AgentCore, RAG, Knowledge Bases, and LLM integrations. The role leads AI platform engineering across model lifecycle management, infrastructure, security, performance, and cost optimization while translating enterprise requirements into secure technical architectures.

Lead the strategic architecture and end-to-end delivery of scalable AI and generative AI solutions. Design backend systems with Python, FastAPI, SQL, and Docker; develop RAG pipelines, AI agents, prompt and context strategies using LangChain and LangGraph; collaborate on React frontends; and provide technical leadership and mentorship to engineering teams.

Design, develop, and deploy scalable AI-powered applications using Python, Azure cloud services, and machine learning models. Build and optimize Microsoft SQL Server databases, integrate Azure infrastructure services, support .NET systems, and develop responsive interfaces with Angular, React, and JavaScript. Responsibilities include architecture, performance tuning, testing, debugging, reliability improvements, and production deployment of intelligent applications.

Designs, develops, deploys, and maintains production-grade AI/ML solutions using deep learning, neural networks, AWS, and Amazon SageMaker. Responsibilities include building ML pipelines, developing chatbots and conversational AI, applying computer vision to image data, monitoring deployed models, and collaborating with teams to translate business needs into AI strategies. Requires 8–12 years of experience and expert-level AWS and machine learning expertise.

Design and build Exterview’s production multi-agent AI platform for candidate screening, interviewing, scoring, and feedback. Integrate Azure OpenAI, multimodal Cognitive Services, Service Bus, Event Grid, Redis, Cosmos DB, Neo4j, and RAG systems. Develop evaluation pipelines, guardrails, adaptive feedback loops, and scalable orchestration for concurrent interviews. Collaborate with backend, frontend, prompt engineering, and product teams while delivering reliable, low-latency AI products.

Freelance AI Specialist responsible for completing and maintaining a shared business knowledge base, improving AI workflows with Claude and Gemini, creating reusable prompts, connecting AI to business tools, documenting processes, training teams, and reporting progress. The role requires careful fact-checking, strong written communication, confidentiality judgment, basic scripting or automation skills, and self-directed project execution.

Seeking senior software engineers with proven expertise in at least one technical area, including AI/ML, LLMs, data, full-stack or backend development, automation, cloud infrastructure, and DevOps. Responsibilities include solving complex problems independently, collaborating across technologies, and delivering production-quality projects in a startup environment. Engagements may be project-based or ongoing.

Lead production AI initiatives including LLM agents, AI drafts, and a shared Context Engine. Own architecture decisions, evaluation systems, guardrails, observability, reliability, and quality measurement. Deliver major AI projects from design through production while providing cross-functional technical leadership and improving engineering standards. Build backend and platform infrastructure using Python, Django, PostgreSQL, Redis, asynchronous workers, and REST or GraphQL APIs.

Own product instrumentation, event taxonomy, metrics, data pipelines, dashboards, experimentation, and predictive modeling. Build analytics for product and marketing decisions, then develop and deploy customer-facing LLM features and secure data-connected agents using retrieval, tool calling, and agent frameworks. Partner with engineering, marketing, product, and leadership to establish Quin’s data and AI foundations across IoT and safety products.

Design, build, and lead scalable backend services and AI platform systems using Node.js, TypeScript, AWS, databases, and modern LLMs. Own architecture, reliability, security, observability, CI/CD, and performance across multiple teams. Mentor engineers, guide technical direction, collaborate cross-functionally, and ship agentic AI products in a startup environment. The role remains hands-on, covering distributed systems, APIs, cloud infrastructure, and end-to-end solution design.

Leads product roadmap strategy, cross-functional project execution, quality management, incident resolution, workflow design, process optimization, and team development. Oversees product lifecycle activities from requirements and prototyping through testing, user acceptance, and deployment. Builds scalable product development operations, manages stakeholders and escalations, mentors team members, and promotes accountability, automation, and continuous improvement.

Three-month unpaid, work-from-office social media training and internship covering self-video creation, AI-assisted video editing, podcasting basics, social media growth techniques, and content production. The schedule is Monday through Saturday, 10 a.m. to 7 p.m., in Basheerbagh.

Advise clients on AI strategy, identify and prioritize use cases, create implementation roadmaps, and guide scalable AI solution delivery. Collaborate with product owners, data scientists, engineers, and business stakeholders while advising on technologies and architectures. Ensure initiatives achieve measurable business outcomes and comply with technical quality, governance, data privacy, ethics, and responsible AI standards.

Three-month unpaid digital marketing internship preceded by free training. Participants will gain practical experience in SEO, social media marketing, affiliate marketing, AI-assisted content creation, Canva and AI design tools, video creation and editing, content writing, analytics, and reporting. The role is work-from-office Monday through Saturday, requires a personal laptop, and may lead to a full-time offer based on performance.

Design and build Meeru AI’s deterministic intelligence engine and conversational AI agent layer for audited financial workflows. Architect grounded, traceable systems using RAG, tool-using agents, structured outputs, verification, human-in-the-loop controls, and automated evaluations. Lead technical direction, mentor engineers, conduct design reviews, and develop production Python services with database and cloud deployment integrations across AWS, Azure, GCP, and on-premises environments.

Build and operate production backend services supporting Supabase AI products, including the MCP server. Responsibilities include service architecture, deployments, scaling, authentication and authorization, observability, reliability targets, on-call incident response, rate limiting, capacity management, and API evolution. The role requires strong TypeScript or Go skills, production backend experience, distributed-systems debugging, and collaboration with Platform and SRE teams in a fully remote environment.

Build production agentic AI solutions and reusable frameworks on Azure AI Foundry. Responsibilities include multi-agent orchestration, RAG knowledge repositories, reusable tools, Dataverse and Power Platform integration, governance, human approvals, evaluation, observability, security, and infrastructure-as-code deployments. The role requires strong Python and C#/.NET engineering skills, production LLM application experience, Azure expertise, and collaboration with business, technical, and risk stakeholders.

Designs and delivers enterprise AI solutions on SAP BTP using SAP AI Core, AI Launchpad, Joule, ML services, and SAP Build Process Automation. Responsibilities include developing AI/ML models, integrating Joule with SAP business applications, delivering intelligent workflows, defining AI architecture and MLOps pipelines, and creating Clean Core-aligned extensions across multi-cloud environments.

Own and lead the company’s end-to-end legal function, including drafting and negotiating commercial agreements, advising on contractual risks, managing vendors and external counsel, supporting employment and equity matters, and handling fundraising, governance, IP, privacy, compliance, and regulatory issues. The role requires strong US commercial contract expertise and experience supporting US-based companies, ideally in pharma, life sciences, or healthcare.