Top Artificial Intelligence Jobs in Hyderabad

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.

Develop, fine-tune, deploy, and optimize AI/ML models and LLM-based RAG solutions using Python and AWS Bedrock. Build REST APIs and microservices with FastAPI or Flask, deploy scalable cloud infrastructure, manage containerized workloads, and develop data pipelines and ETL processes. Work with relational and NoSQL databases, CI/CD, and AWS services while collaborating with technical and product teams and documenting AI workflows.

Designs and leads enterprise AI Factory and GPU infrastructure architectures spanning compute, networking, storage, Kubernetes, cloud, security, observability, and AI/ML platforms. Assesses workloads, develops reference architectures and detailed designs, performs capacity planning and technology evaluations, leads proof-of-concepts, and supports deployment, migration, troubleshooting, and production transition. Collaborates across infrastructure, security, cloud, data, application, and operations teams to ensure scalable, secure, reliable, and cost-effective AI platforms.

Own the Workato environment, configure enterprise integrations, and build AI-native packages using Workato AI Genie. Design and deploy agent-ready workflows and AI-powered workflow automation solutions, while managing RBAC, auditing, and multi-workspace environments. Integrate Workato with enterprise applications and support scalable workflow implementations.

Creates technical documentation, implementation guides, whitepapers, product content, landing pages, and blog posts focused on AI infrastructure and enterprise technology. Researches complex technologies, translates specifications and architectures into clear content, collaborates with technical experts, validates accuracy, and maintains consistent documentation standards for IT professionals, engineers, architects, and technology decision-makers.

Build and deploy production AI systems across LLM orchestration, retrieval, agents, evaluations, voice, APIs, backend services, and infrastructure. Design reliable LLM pipelines, evaluation suites, data flows, and real-time multimodal experiences. Experiment with emerging models, debug issues across the stack, and communicate technical decisions clearly while owning projects from exploration through production iteration.

Work directly with pharmaceutical customers to understand workflows and design, prototype, integrate, and deploy AI-powered solutions. Translate ambiguous requirements into technical architectures, build Python-based integrations and data workflows, lead technical discovery and implementation workshops, debug APIs and cloud deployments, and collaborate with customer and internal engineering teams. Bring recurring customer needs to product and engineering while supporting regulated enterprise AI deployments.

Build production-grade AI applications for pharmaceutical commercial, medical, and patient workflows using LLMs, retrieval, agents, structured data, and multimodal AI. Develop scientific knowledge systems, evaluation frameworks, and reliable, safe AI solutions using healthcare and life sciences datasets. Collaborate with pharma customers and domain experts to translate complex workflows into scalable technical products.

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.

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.

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.

Audit AI agent call and chat transcripts against quality and compliance standards, identify regressions and edge cases, document actionable engineering issues, and participate in client QA and audit calls. Reconcile internal findings with client audits, explain performance data, and provide recurring quality insights to the Technical Program or Product Manager for release communications and client reviews.

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.

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.

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.

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.