Top Artificial Intelligence Jobs in Hyderabad

Build and ship product features for government procurement and contract lifecycle software. Responsibilities include writing efficient Java code, applying data structures and algorithms, designing and managing SQL and NoSQL databases, using AI tools, debugging, testing, and owning software end to end.

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.

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.

Design and build scalable infrastructure for production-grade generative AI agents, including memory, context, state, session management, orchestration, agent-to-agent communication, tool integration, observability, reliability, security, and cost optimization. Collaborate with application, platform, and product teams to establish reusable frameworks and architectures for secure enterprise agentic systems.

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.

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.

Designs enterprise AI architectures and production-ready Agentic AI, Generative AI, and AI-native solutions for Energy Trading & Risk Management. Responsibilities include client consultation, architecture governance, AI platforms and roadmaps, RAG and multi-agent systems, MLOps and LLMOps, data pipelines, security, compliance, observability, and responsible AI guardrails.

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.

Leads the architecture, governance, integration, and delivery of AI-driven solutions across enterprise CRM, ERP, and iPaaS platforms. Designs AI/ML-enabled backend workflows, APIs, event-driven systems, automation pipelines, and production environments. Partners with technical and business stakeholders, ensures scalability, security, and compliance, and communicates architecture decisions. The role requires extensive enterprise architecture and systems integration experience, production AI implementation expertise, and knowledge of platforms such as Salesforce, SAP, MuleSoft, and related technologies.

Build AI-assisted browser automation and automated tests for web applications, APIs, and data workflows using Playwright, Stagehand, and Python. Integrate LLM APIs, CI/CD pipelines, monitoring, logging, metrics, and reporting dashboards. Develop reusable automation frameworks, evaluate deterministic versus AI-driven approaches, improve reliability, and optimize AI performance and costs.

Own Apty’s AI-first SEO strategy across Google and AI search platforms. Lead keyword and intent mapping, content structure, technical and on-page optimization, internal linking, entity-based SEO, and structured data. Partner with content and engineering teams to improve discoverability, rankings, AI citations, conversions, and organic pipeline contribution. Measure success through business impact rather than traffic or content volume.

Design, build, test, and deploy production-grade agentic AI systems using Python, LangGraph, LangChain, AWS Bedrock, and orchestration tools. Integrate AI agents with APIs and business systems, implement memory and retrieval, establish observability and safeguards, and develop reusable SDKs and templates. Collaborate directly with clients to define requirements, run experiments, and rapidly deliver reliable AI solutions using modern software engineering, cloud deployment, and CI/CD practices.

Conduct AI visibility audits across ChatGPT, Perplexity, and Google AI Mode; identify content, schema, and technical improvements; use AI tools to run scripts; prepare client reports and updates; and document repeatable processes. The role involves live client work, communication, experimentation, and learning SEO, analytics, and AI visibility tools on the job.

Build governed Databricks Lakehouse foundations and productionize machine learning and GenAI solutions, including forecasting, anomaly detection, NLP, RAG, and predictive analytics. Develop reliable batch and streaming workflows, CI/CD, monitoring, governance, security, lineage, and semantic models. Collaborate with product, data, platform, and domain teams to deliver measurable customer value while meeting reliability, compliance, cost, and operational objectives.

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.

Investigate escalated production incidents (DB anomalies, failed payments, data migrations), write post-mortems and runbooks, and build AI-powered automation (LLM diagnostics, agents) plus developer tooling. Maintain CI/CD, GCP infrastructure, container deployments, observability, and partner on ERP and payment provider integrations.

Work hands-on with generative AI tools to design, test, document, and maintain reusable AI-enabled consulting workflows. Build prompts, Claude skills/custom assistants, low-code automations, and knowledge systems; validate AI outputs; produce written analysis, Excel modeling, and professional PowerPoint deliverables to support U.S. and India consulting engagements.

Develop agentic AI and generative AI solutions using Python, FastAPI, LLMs, RAG, AI agents, LangChain, and LangGraph. Build and deploy cloud-based applications on GCP, integrate vector databases, and implement CI/CD pipelines. The role requires strong software engineering experience and hands-on experience with agentic AI and GenAI technologies.

Coordinates the AI Accountability Network’s newsletters, training programs, grants, fellowships, payments, events, communications, and community initiatives. Manages editorial workflows, contractors, applications, logistics, websites, listservs, dashboards, metrics, and stakeholder communications. Supports grant administration, funder reporting, travel, curriculum development, and website publishing while improving processes and tracking program impact.

Design, build, and scale production AI agents across EveryWatch. Own the agent roadmap, architecture, shared tools, memory, state, observability, evaluation, and cost controls. Harden WatchChat, develop RAG and tool-calling systems, establish measurable quality standards, and ship solutions directly in Python. Collaborate with sales, product, data, and engineering teams to identify high-impact use cases and take agent concepts from discovery through production.

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.