Top Artificial Intelligence Jobs in Hyderabad

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.

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.

Own end-to-end architecture for AI/ML and generative AI customer engagements on AWS. Design Well-Architected solutions using services such as Amazon Bedrock and SageMaker, lead discovery workshops and architecture reviews, support AWS co-sell opportunities, prototype with AI-assisted coding tools, mentor technical teams, evaluate emerging services, and develop reusable architectures and playbooks.

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.

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.

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 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 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.

Architect and improve autonomous AI optimization algorithms for a marketing platform. Analyze A/B tests, integrate AI models, develop multi-touch attribution, expand advertising-channel capabilities, reduce inference costs and latency, incorporate customer feedback, and maintain reliable, continuously improving optimization loops.

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.

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.