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AI/ML Engineer
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Key Responsibilities:
Re-Imagine Testing with algoQA The Digital economy is pushing software companies to refresh products in short order. Companies are under tremendous cost-and-time pressure. Faster time-to-market with no compromise on quality & reliability, at a lower cost, is the need. Software testing in such a scenario needs to be quick and responsive to the constantly evolving software. This is where algoQA, by Algoshack Technologies steps-in. algoQA is a born-agile platform that transforms software quality engineering by enabling a design first outlook. We have re-imagined testing with algoQA, a low code, no code platform that can auto generate test cases and test scripts that can be played back in technologies of your choice, giving you the freedom to not be locked-in to an automation platform. The low code / no code, easy to learn platform is technology agnostic, and covers Web, Desktop, Mobile & Hybrid Applications, as well as applications for Robotic Process Automation and Embedded technologies. It supports UI Based Test Automation, API Test Automation and Load Test Automation, by producing an optimal number of test cases written in Gherkin language along with datasets, and executable test scripts. The platform produces scripts in the technology of choice and the scripts can be executed anywhere. All it takes is a simple profiling of the application under test using Drag & Drop & Wizards. Join Us Be a part of this incredible team as we disrupt software delivery for the better.
Job ID: 128645613
Skills:
Image Processing, Data Science, deep learning frameworks, chatbot development, production-quality AI pipelines, generative AI models, AI applications, neural network architectures, cloud AI services, on-premises infrastructure
Skills:
workflow engines , Python, tool function calling, tracing observability tooling, Logfire, AI systems in production, prompting, Temporal, OpenTelemetry, pydantic-ai, LiteLLM, MCP, agent architectures
Skills:
Apis, Performance Monitoring, Version Control, Gcp, MLops, Azure, Python, Statistical Analysis, Machine Learning Algorithms, AWS, data preprocessing, cloud platforms, production-ready ML pipelines, model evaluation, deep learning techniques, feature engineering, model lifecycle management, model optimization, hyperparameter tuning
Skills:
AWS EKS, FastAPI, Kubernetes, Python, Celery, Sqlalchemy, Airflow, Claude API, confidence calibration techniques, Pydantic, LLM output validation frameworks, embedding models, Weaviate, pgvector, Pinecone, semantic search, RAG context
Skills:
triton , Pytorch, Kubernetes, Python, Docker, Qdrant, vLLM, LlamaIndex, Airflow, Weaviate, pgvector, LangChain, TGI, CrewAI, Pinecone, LangGraph, GPTQ, MLOps tools, Kubeflow, MLflow, quantization techniques, Scikit-learn, AWQ, Hugging Face ecosystem, AutoGen, GGUF, Weights Biases, FAISS
