We're seeking an experienced Python/AI Engineer to research, customize, and deploy machine learning models across audio, vision, and language domains. You'll work on production systems that process multimodal data at scale and build automation solutions for our data platform serving global customers.
Candidates should have
8+ years
of software engineering experience, including 5+ years of hands-on Python development and 4+ years of experience in one or more of the following: LLM/Generative AI, Conversational AI (Audio), or Computer Vision.
Key Responsibilities:
Research, evaluate, customize and deploy open-source ML models (audio, vision, and LLMs) for production use cases on AWS
Design and implement LLM-powered systems, including prompt engineering, prompt optimization, and workflow orchestration for real-world applications
Develop and evaluate Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, embedding strategies, retrieval quality, and response evaluation
Build evaluation frameworks for prompts, LLM outputs, and RAG systems using automated metrics, benchmarking datasets, and human-in-the-loop validation
Develop APIs and services using FastAPI, Django, or similar frameworks to expose ML and AI capabilities
Design automation solutions and scripts for data processing, validation, and quality control across multimodal datasets (audio, image, video, text)
Provide technical leadership on AI architecture decisions, model selection, prompt design strategies, and system design trade-offs
Drive technical initiatives from concept to production, including feasibility analysis, prototyping, experimentation, and deployment strategy
Mentor junior ML engineers through code reviews, technical guidance, and knowledge sharing
Collaborate with cross-functional teams to translate research outcomes into scalable, production-ready AI systems
Qualifications & Experience:
Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field
Software engineering experience in Python, including object-oriented design and common software design patterns
Experience building production services and RESTful APIs using Python frameworks such as FastAPI, Django, Celery, or Streamlit
Solid understanding of system design, distributed architectures, and cloud environments (AWS, GCP, or Azure)
Experience with machine learning and deep learning frameworks such as PyTorch, TensorFlow, Keras, and Scikit-learn
Experience processing and analyzing large-scale unstructured and multimodal datasets (audio, image, video, and/or text)
Experience developing automation scripts and tools for data pipeline management, data processing, and validation
Experience researching, evaluating, customizing, and deploying open-source ML models (audio, vision, and LLMs) for production use cases
Hands-on experience developing and deploying Generative AI/LLM applications with expertise in prompt engineering, RAG systems, vector databases, and frameworks such as LangChain or LangGraph
Understanding or experience with LLM evaluation frameworks using open-source tools for automated and human-in-the-loop evaluation workflows
Understanding of MLOps practices, including model versioning, experiment tracking, deployment, and monitoring
Experience implementing testing strategies for production AI systems to ensure reliability, scalability, and performance
Strong communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders
Demonstrated ability to mentor engineers, lead technical initiatives, and drive projects from research to production in cross-functional environments
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Candidates should have
8+ years
of software engineering experience, including 5+ years of hands-on Python development and 4+ years of experience in one or more of the following: LLM/Generative AI, Conversational AI (Audio), or Computer Vision.
Key Responsibilities:
Research, evaluate, customize and deploy open-source ML models (audio, vision, and LLMs) for production use cases on AWS
Design and implement LLM-powered systems, including prompt engineering, prompt optimization, and workflow orchestration for real-world applications
Develop and evaluate Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, embedding strategies, retrieval quality, and response evaluation
Build evaluation frameworks for prompts, LLM outputs, and RAG systems using automated metrics, benchmarking datasets, and human-in-the-loop validation
Develop APIs and services using FastAPI, Django, or similar frameworks to expose ML and AI capabilities
Design automation solutions and scripts for data processing, validation, and quality control across multimodal datasets (audio, image, video, text)
Provide technical leadership on AI architecture decisions, model selection, prompt design strategies, and system design trade-offs
Drive technical initiatives from concept to production, including feasibility analysis, prototyping, experimentation, and deployment strategy
Mentor junior ML engineers through code reviews, technical guidance, and knowledge sharing
Collaborate with cross-functional teams to translate research outcomes into scalable, production-ready AI systems
Qualifications & Experience:
Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field
Software engineering experience in Python, including object-oriented design and common software design patterns
Experience building production services and RESTful APIs using Python frameworks such as FastAPI, Django, Celery, or Streamlit
Solid understanding of system design, distributed architectures, and cloud environments (AWS, GCP, or Azure)
Experience with machine learning and deep learning frameworks such as PyTorch, TensorFlow, Keras, and Scikit-learn
Experience processing and analyzing large-scale unstructured and multimodal datasets (audio, image, video, and/or text)
Experience developing automation scripts and tools for data pipeline management, data processing, and validation
Experience researching, evaluating, customizing, and deploying open-source ML models (audio, vision, and LLMs) for production use cases
Hands-on experience developing and deploying Generative AI/LLM applications with expertise in prompt engineering, RAG systems, vector databases, and frameworks such as LangChain or LangGraph
Understanding or experience with LLM evaluation frameworks using open-source tools for automated and human-in-the-loop evaluation workflows
Understanding of MLOps practices, including model versioning, experiment tracking, deployment, and monitoring
Experience implementing testing strategies for production AI systems to ensure reliability, scalability, and performance
Strong communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders
Demonstrated ability to mentor engineers, lead technical initiatives, and drive projects from research to production in cross-functional environments
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