Top Artificial Intelligence Jobs in Hyderabad

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 and implement end-to-end AI/ML systems for robotic perception and control: research and build CV modules, train and benchmark models, prototype in simulation, transfer to real robots, create preprocessing/postprocessing pipelines in Python/C/C++, and optimize for real-time edge deployment.

Lead and align Engineering, ML, and Analytics to deliver scalable, reliable production AI and data systems. Own technical direction, architecture, MLOps, and platform reliability while partnering with Product, Sales, and Customers to ensure engineering work maps to measurable business outcomes.

Own enterprise AI engagements from discovery through production deployment. Design, build, integrate, debug, evaluate, and harden agentic systems using LLMs, RAG, tool calling, workflows, and guardrails. Lead technical workshops, proofs of concept, solution scoping, and executive ROI discussions. Measure adoption, containment, accuracy, and cost outcomes; create reusable delivery assets; enable partners; and provide product feedback. The role is customer-embedded and requires sustained on-site presence during engagements.

Build and operate backend services and infrastructure for LLM- and voice-driven healthcare applications. Implement APIs, productionize LLM workflows, deploy cloud containerized services, and monitor performance and reliability end-to-end.

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.

Develop, fine-tune, deploy, and optimize AI/ML models and LLM-based RAG systems using Python, AWS Bedrock, and major ML frameworks. Build scalable REST APIs and microservices with FastAPI or Flask, deploy containerized solutions on AWS, and manage CI/CD. Design databases and automated ETL pipelines, integrate cloud services, and document AI workflows while collaborating with cross-functional technical and product teams.

Design, build, and optimize AI-driven automation workflows using platforms like n8n, Make, and Zapier. Train, fine-tune, and deploy custom LLMs (OpenAI, Hugging Face, TensorFlow) and integrate LLM APIs. Collaborate with stakeholders, troubleshoot and document workflows, and stay current on generative AI and automation technologies.

Design, build, and optimize AI-driven automation workflows using platforms like n8n, Make, and Zapier. Train, fine-tune, and deploy custom LLMs (OpenAI, Hugging Face, etc.), integrate LLM APIs, collaborate with stakeholders, troubleshoot and document solutions, and stay current on generative AI advances. Mandatory technical assessment required.

The AI Creative Designer creates high-quality, AI-generated visuals for marketing campaigns, collaborating with teams to optimize visual content for conversion rates.

The Senior AI/ML Engineer will design, develop, and deploy language models for African languages, focusing on NLP applications and cultural context.

The AI Product Manager will lead product discovery and strategy, prototype solutions using AI tools, perform competitive analysis, track product metrics, advocate for user experience, and collaborate with different teams.

The Applied AI Researcher will design experiments, train models, and build pipelines for a GenAI platform while contributing to research and deploying systems in production.

Responsible for leading end-to-end AI research projects, developing domain-specific models, mentorship, and optimizing research-to-production processes while collaborating across disciplines.

The Principal Applied AI Researcher will define and lead domain-specific model strategies, manage model lifecycle, mentor researchers, and influence platform decisions in AI research.

Assist in developing AI solutions for financial planning, working on projects involving data structures, algorithms, and integrating AI features into the platform.

Build the AI layer for SigNoz: implement agent orchestration, long-running execution, streaming APIs (SSE), and MCP server integrations to expose observability data to LLM agents. Ship production LLM features, design scalable distributed backends in Go and async Python, and drive projects end-to-end in a remote-first environment.

This job is for candidates with AI infrastructure experience to submit their resumes for future opportunities. No specific role is posted.

As a Forward Deployed Engineer, drive customer success through AI deployments, optimize systems for reliability, and collaborate with teams to enhance products.

Build, deploy, and maintain AI/ML and generative AI solutions and platforms. Create CI/CD pipelines, containerized workloads (Docker/Kubernetes), and IaC (Terraform). Support cloud deployments (AWS/Azure/GCP), observability (Prometheus/Grafana), and MLOps practices while collaborating with engineering teams for reliability, performance, and security.

Lead product quality across the SDLC by reducing defects, driving root-cause analysis, defining automation strategy and quality gates, building automated test frameworks (Playwright, API, integration, performance), integrating tests into CI/CD, enforcing release readiness, and using Claude Code and other AI tools to analyze issues and improve engineering outcomes.

Own product lines of agent-based AI systems: write machine-readable specs, design prompt/tool/schema behavior, build HTML prototypes with live model calls, run evaluations, choose model tradeoffs, instrument usage/quality/cost, and work closely with engineering, GenAI, design, and enterprise customers to ship measured outcomes.

Own and design the shared AI system architecture for production GenAI products: retrieval/RAG, agentic workflows, model selection/fallbacks, evaluation/regression testing, and production readiness. Build reusable patterns, mentor engineers, and ensure reliability, cost-awareness, and safe failure modes across products.

Design, build, and deploy AI agents and machine learning models; integrate ML/NLP into full-stack applications; develop APIs and data pipelines; deploy and manage services on AWS; collaborate with frontend/backend teams and maintain CI/CD, databases, and production monitoring.

Develop and optimize AI models for efficient inference on custom silicon. Build fine-tuning pipelines, implement acceleration techniques (quantization, sparsity, distillation), create benchmarking and profiling tools, and collaborate with hardware, compiler, and quantization teams to translate algorithmic improvements into real hardware gains.