Top AI & Machine Learning Jobs

Design and implement scalable microservices-based backend systems for a UC platform, integrate LLM/GenAI (RAG) chatbots, build AI data and ELT pipelines with vector DBs, manage Kubernetes/Docker infrastructure and observability, and optimize systems for high-volume traffic.

Research, invent, implement, and evaluate signal-processing and neural-network video coding tools; implement prototypes in C++/Python; run large-scale evaluations; contribute to JVET/MPEG standardization; author patents and publications; provide technical leadership and supervise research.

Research, invent, implement, and evaluate novel video coding tools (signal-processing and neural approaches). Develop software prototypes (C++/Python), run large-scale evaluations, contribute to standardization (JVET/MPEG), author patents/publications, and collaborate with industry and academia to shape next-generation video coding for 6G and AI-native media.

Build and test AI agents for code assistance, documentation, test automation, and troubleshooting; integrate agents with internal tools and APIs; follow engineering practices; produce adoption guides.

Design and implement ML/AI solutions (predictive models, recommenders, anomaly detection, deep learning) using Python, PySpark and SQL on on-premise and cloud platforms. Collaborate with business stakeholders, define success metrics, optimize technical requirements, and mentor junior data scientists.

The Technology Architect will lead AI architecture governance, establish standards for AI applications, and ensure alignment with enterprise goals. Responsibilities include overseeing production AI deployments, promoting best practices, and collaborating on AI initiatives across the organization.

Senior customer-facing AI leader who defines AI strategy, architects end-to-end LLM and RAG-based solutions, leads technical discovery and proof-of-value, drives enterprise AI adoption and governance, influences product roadmaps, and ensures successful production deployment and measurable business value across cloud-native and telecom environments.

Lead design and development of AI-enhanced baseband solutions across PHY/L1, L2 and scheduling. Identify AI opportunities, prototype and evaluate ML algorithms, optimize for RAN compute and accelerators, validate via simulation/lab/over-the-air, collaborate cross-functionally, scout technologies, and mentor peers to bring prototypes into deployable solutions.

Six-month AI/ML internship to research and implement MARL communication architectures and semantic communication protocols for Physical AI. Design experiments, develop world-model-based representations, evaluate under realistic wireless constraints using MARL benchmarks and Mobile-Env, and contribute to research publications.

The role involves designing and developing AI solutions, optimizing AI models, analyzing data for insights, and collaborating with teams to integrate AI into products.