Top Artificial Intelligence Jobs in Hyderabad

Build and lead the AI engineering platform for enterprise COBOL modernization. Responsibilities include designing transformer models for COBOL-to-Java/Python translation, developing high-performance code analysis engines, optimizing inference, architecting distributed cloud-native systems, and delivering reliable AI pipelines for large enterprise codebases. The role also includes hiring and leading engineering teams, shaping product strategy, and partnering with the CEO as a technical co-founder.

Hiring for multiple software engineering opportunities in Hyderabad, including fresher and experienced positions across Java, testing, Python, DevOps, AWS, full-stack development, data science, AI/ML, cybersecurity, Node.js, and React.js.

Own the backend and agent systems powering LLM products, including multi-turn conversations, tool calling, RAG, state management, evaluations, monitoring, and production reliability. Diagnose failures across models, knowledge systems, tools, and infrastructure; establish regression testing and quality measurement; and guide product and technical decisions. The role requires shipping and improving LLM features at scale, strong backend engineering, disciplined evaluation practices, and preferably Web3 experience.

Build and integrate AI agents, APIs, webhooks, databases, and business-system automations. Translate business processes and customer flows into simple, efficient workflows with minimal code. This is a remote, part-time individual-contributor consulting engagement requiring hands-on system building and immediate availability.

Leads enterprise Microsoft 365 transformation initiatives focused on SharePoint Online, Power Platform, Copilot, AI agents, automation, security, compliance, and governance. Architects and implements collaboration solutions, migrates legacy platforms, configures Purview and related controls, and develops AI governance strategies. Provides technical leadership, client engagement, workshops, training, and ongoing support while coordinating project risks and dependencies. Requires availability during Eastern Time business hours and deep Microsoft 365, security, programming, database, and cross-tenant migration expertise.

Design and lead enterprise agentic AI architectures on AWS, including multi-agent workflows, RAG solutions, LLM integrations, and API-based tools. Build production systems with Bedrock, AgentCore, SageMaker, LangGraph, CrewAI, and LlamaIndex. Establish security controls, identity management, guardrails, observability, tracing, evaluation pipelines, and managed-service deployments. Collaborate with enterprise teams and communicate architecture decisions effectively.

Lead the design and delivery of AI/ML and Generative AI solutions, including RAG systems, LLM applications, prompt-driven agents, automation pipelines, and proof-of-concepts. Develop and fine-tune models, evaluate AI frameworks and cloud services, establish responsible AI practices, guide technical best practices, collaborate with stakeholders, and ensure enterprise security and compliance.

Leads the design, development, deployment, and optimization of scalable AI/ML solutions, including generative AI, LLM, NLP, and RAG applications. Drives architecture and solution design, develops production-ready models, collaborates with engineering and R&D teams on proofs of concept and deployments, ensures application scalability and reliability, and mentors engineers.

Develop AI applications using Python, generative AI, LLMs, RAG, vector databases, APIs, and agentic AI frameworks. Build and integrate solutions with models including GPT, Claude, Gemini, and Llama. Use LangChain, LangGraph, Docker, cloud platforms, and modern deployment tools. The role requires strong problem-solving, API development, prompt engineering, and immediate or short-notice availability for office-based work.

Design and develop scalable AI applications, LLM-powered services, real-time voice and conversational AI solutions, APIs, and microservices using Python, FastAPI, and Django. Build RAG, embeddings, vector database, AI assistant, and workflow automation systems. Optimize prompts, context, token usage, and model costs while implementing safety guardrails and evaluation frameworks. Collaborate with engineering and product teams to deliver production-ready solutions.

Lead end-to-end AI Data Assurance and Quality Engineering programs, managing scope, timelines, budgets, risks, resources, governance, testing frameworks, quality metrics, and release readiness. Collaborate with business, data engineering, architecture, and AI/ML teams while mentoring quality and assurance teams. Drive automation, AI-assisted testing, workforce planning, client pursuits, solutioning, capability development, innovation, and practice growth.

Develop full-stack AI and Generative AI applications using ReactJS, Python, FastAPI, REST APIs, LLMs, RAG, and Agentic AI. The role requires integrating frontend and backend systems, building AI-powered solutions, managing code with Git/GitHub, and working in a hybrid environment across Bangalore, Hyderabad, Pune, or Chennai.

Lead a technical pod delivering Java full-stack applications with React, Python, AI tools, Spring Boot, microservices, and REST APIs. Provide technical team leadership, guide cloud implementations with a preference for Azure, engage directly with clients, and communicate and present effectively. The role is based in Bangalore or Hyderabad and initially requires five days per week in the office during the general shift.

Build and deploy enterprise AI applications, data pipelines, and GenAI solutions using Python, LLMs, and RAG. Develop backend services and microservices integrating REST APIs, SQL, NoSQL databases, and enterprise data platforms. Optimize AI models for production environments while solving complex technical problems and collaborating effectively with stakeholders.

Lead the development of enterprise AI solutions using Generative AI, LLMs, RAG, and Agentic AI. Build AI applications, chatbots, and retrieval systems using embeddings and vector databases. Apply machine learning frameworks, deploy and monitor AI models, establish CI/CD pipelines, and design scalable enterprise AI architectures across cloud platforms.

Develop Generative AI solutions using Python and FastAPI. Build and integrate machine learning, deep learning, NLP, LLM, prompt engineering, and RAG systems. Work with text embeddings and vector databases including FAISS, Chroma, and Pinecone. Implement model inference, deployment, REST APIs, and backend integrations. The role is hybrid in Hyderabad or Bangalore and requires candidates with 4.5+ years of relevant experience who are currently serving notice and can join within 15 days.

Leads the development of scalable, production-ready AI solutions using machine learning, generative AI, NLP, deep learning, neural networks, and cloud AI platforms. The role requires Python, TensorFlow, PyTorch or Keras, prompt engineering, Hugging Face, spaCy or NLTK, and data visualization tools. It also involves applying statistical modeling, working with Agile/Scrum methodologies, and collaborating with stakeholders.

Develop production-grade agentic and generative AI applications using LLMs, RAG, vector databases, AI agent frameworks, prompt engineering, REST APIs, and cloud AI platforms. Build AI copilots, conversational AI, multi-agent systems, and knowledge management solutions for enterprise applications.

Build and ship AI-powered features for fashion, beauty, and home using large-scale product, image, and consumer data. Prototype with LLMs, agents, multimodal and CV models, run experiments, measure impact, and take models end-to-end from hypothesis to production while collaborating with product, design, and business teams.

Assist with developing and testing AI and machine learning solutions using Python and relevant libraries. Support data preparation, model development, evaluation, debugging, and documentation. Explore generative AI, LLMs, and AI-based applications while collaborating with the technical team on real-world problems. This is a three-month, office-based internship in Hyderabad with potential full-time employment based on performance.

Build and manage production AI agents, high-precision RAG pipelines, and LLM-powered features. Develop backend architecture using Postgres, REST APIs, asynchronous programming, queues, storage, RBAC, and encryption. Integrate foundation models, establish evaluation patterns, write automated tests, monitor system health, and collaborate with product managers on reliable, secure AI applications.

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.

Develop and deploy scalable Java applications across the full software development lifecycle. Build Spring and Spring Boot microservices, REST APIs, and AI-integrated features using OpenAI, Claude, and automation tools. Prototype frontend interfaces with Lovable, troubleshoot existing systems, collaborate with cross-functional teams, mentor junior developers, and contribute to engineering best practices.

Build production front-end and full-stack applications for conversational, agentic, and generative AI experiences. Design real-time streaming interactions using WebSockets and SSE, create agent-readable interfaces, integrate LLM and orchestration backends, and deliver accessible, performant React and TypeScript applications. The role also shapes AI-native interaction patterns, mentors teams, and ensures compliance, security, reliability, and trust in a regulated environment.