Top Machine Learning Jobs in Hyderabad

The Customer Success Business Partner at Lyric drives customer adoption and retention for strategic accounts, leveraging expertise in AI and supply chain operations.

The role requires a skilled Machine Learning Engineer to create complex models and AI solutions while collaborating with teams to enhance products and services.

We are seeking a Director/VP of Sales for the USA market to develop sales strategies, lead a team, drive revenue, and build customer relationships in the cybersecurity sector.

Design, build, and deploy machine learning models for a payments platform, overseeing end-to-end solutions and experimentation, while monitoring models in production.

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.

Train and deploy small, task-specific ML models for realtime English conversational-skill applications. Create datasets and task-specific evaluations, improve internal SOTA, develop ASR and speech-feedback metrics, iterate experiments, ship production systems, and collaborate cross-functionally while helping hire as the team grows.

Develop and refine voice-optimized prompts and agentic LLM workflows for finance use cases. Build RAG grounding, integrate LLMs with voice/telephony (LiveKit), implement feedback loops, A/B testing, fine-tune models, and collaborate on production ML services and OCR/chatbot components.

Design, develop, fine-tune, and productionize ML and Generative AI/LLM solutions. Build end-to-end ML pipelines (data ingestion, modeling, deployment, monitoring), implement RAG and vector DB architectures, apply prompt/context engineering and MLOps best practices, and collaborate with cross-functional teams to optimize models for performance, latency, scalability, and cost.

Design, train, and productionize state-of-the-art object detection systems. Build robust training pipelines, perform error analysis and ablations, optimize inference for latency and memory, and deliver production services with Docker, CI/CD, and APIs. Implement testing, monitoring, model versioning, and retraining pipelines. Rapidly prototype research ideas into deployable components and maintain clear technical documentation.

Lead end-to-end ML systems: data pipelines, model development (transformer-based detection), sim-to-real synthetic data, multi-sensor fusion, on-device real-time inference (Jetson), MLOps (experiment tracking, versioning), and production deployment and monitoring. Set ML roadmap and hire/lead as the team grows.

The Senior Machine Learning Engineer will design, train, and deploy ML models for optimizing revenue in advertising, taking ownership of product development and leading the ML discipline.

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 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.

Design, develop, and deploy ML models and pipelines; preprocess data and engineer features; implement MLOps (Docker, Kubernetes, CI/CD); collaborate with stakeholders to translate business problems into AI solutions; monitor and optimize model performance and maintain documentation and governance.

Deploy and productionize ML models on GCP Vertex AI for real-time and batch inference. Build scalable pipelines, perform feature engineering, monitor model performance and drift, support ML-Ops and governance, and collaborate with teams on retail banking (credit risk, marketing, customer service) use cases.

Design and implement AI/LLM-driven data extraction and ETL pipelines, build web crawling and API integrations, implement RAG systems, mitigate LLM hallucinations, evaluate third-party tools, and collaborate with content and analytics teams to deliver high-quality, scalable data for downstream analysis.

Lead design and implementation of enterprise-scale generative AI and agentic systems. Architect multi-agent orchestration, RAG and inference pipelines, fine-tuning/quantization strategies, integrations (APIs, webhooks, connectors), and secure data/model management. Optimize deployments across cloud, edge, and on-premises while establishing evaluation, selection, and lifecycle practices.

Develop, deploy, and scale AI/ML and generative AI solutions for a fintech platform. Design data models and pipelines, build agentic frameworks, integrate OCR and vector DBs, implement RAG, and champion MLOps and cloud deployment practices while mentoring teammates.

The Senior ML Research Engineer will develop computer vision algorithms for item authentication, mentor team members, and oversee model deployment.

Develop and optimize machine learning models, manage project priorities, and improve real-world solutions using advanced ML techniques.