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Showing 10 jobs
Skills:
Tensorflow, Ml, Java, Kotlin, Kubernetes, Sql, Python, Microservices, AWS, LLMs, Ai
Skills:
Gcp, Azure, AWS, AWS Bedrock, AI engineering, MCP automation frameworks, access control for AI systems, Anthropic, Azure OpenAI, output validation monitoring, cloud environments, agent-driven workflows, AI security controls, machine learning engineering, AI assistants, GPT-based tools, OpenAI
Skills:
Sql, Gcp, Neo4j, Azure, Python, AWS, LangChain, LLMs, Cypher, LangGraph, graph query languages, RAG agents, Amazon Neptune, Knowledge Graphs
Skills:
Java, Distributed Systems, Api Integration, Security Controls, PostgreSQL, Python, Microservices, AI ML Integration, Event-Driven Patterns, Domain-Driven Design
Skills:
Rtp, Microservices, Kafka, Asterisk, Node.js, Kubernetes, Python, Docker, vector DBs, LLMs, Redis Streams, message brokers, Go, ASR, CI CD, SIP, CPaaS, TTS, freeswitch, WebSockets
Skills:
Cloud Automation, Databricks, Sql, Python, AWS, Etl, Data Quality Governance, Agentic AI, Delta Lake, Architecture Design, Performance Cost Optimization
Skills:
lifecycle management , Devops, evaluation methods, AI application engineering, enterprise AI platforms, feedback loops, Monitoring, cost-aware AI design, build and release workflows, observability, operationalizing AI-enabled systems, agentic systems, platform integration, CI CD pipelines, workflow automation
Skills:
Java, Distributed Systems, Framework Design, Technical Leadership, Authorization Frameworks, Enterprise Security Patterns
Skills:
Distributed Systems, Cloud Infrastructure, rollback experimentation, GenAI models, automated regression frameworks, real-time performance monitoring, caching strategies, model serving infrastructure, AI capability routing and orchestration, ML inference infrastructure, latency optimization, observability tooling, evaluation and quality assurance infrastructure, large-scale AI platform engineering
Skills:
Distributed Systems, Cloud Infrastructure, real-time performance monitoring, observability tooling, GenAI models, large-scale AI platform engineering, automated regression frameworks, AI capability routing and orchestration, model serving infrastructure, ML inference infrastructure, evaluation and quality assurance infrastructure, latency optimization, rollback experimentation, caching strategies
