Fractal Analytics

Building the machine learning production (or MLOps) is the biggest challenge most large companies currently have in making the transition to becoming an AI-driven organization. This position is an opportunity for an experienced, server-side developer to build expertise in this exciting new frontier. You will be part of a team deploying state-of-the-art AI solutions for Fractal clients.

Responsibilities

As MLOps Engineer, you will work collaboratively with Data Scientists and Data engineers to deploy and operate advanced analytics machine learning models. You’ll help automate and streamline Model development and Model operations. You’ll build and maintain tools for deployment, monitoring, and operations. You’ll also troubleshoot and resolve issues in development, testing, and production environments.

• Enable Model tracking, model experimentation, Model automation
• Develop ML pipelines to support
• Develop MLOps components in Machine learning development life cycle using Model Repository (either of): MLFlow, Kubeflow Model Registry
• Develop MLOps components in Machine learning development life cycle using Machine Learning Services (either of): Kubeflow, DataRobot, HopsWorks, Dataiku or any relevant ML E2E PaaS/SaaS
• Work across all phases of Model development life cycle to build MLOPS components
• Build the knowledge base required to deliver increasingly complex MLOPS projects on Azure
• Be an integral part of client business development and delivery engagements across multiple domains

Required Qualifications

• 3-5 years experience building production-quality software.
• B.E/B.Tech/M.Tech in Computer Science or related technical degree OR Equivalent
• Strong experience in System Integration, Application Development or Data Warehouse projects across technologies used in the enterprise space
• Knowledge of MLOps, machine learning and docker
• Object-oriented languages (e.g. Python, PySpark, Java, C#, C++)
• CI/CD experience( i.e. Jenkins, Git hub action,
• Database programming using any flavors of SQL
• Knowledge of Git for Source code management
• Ability to collaborate effectively with highly technical resources in a fast-paced environment
• Ability to solve complex challenges/problems and rapidly deliver innovative solutions
• Foundational Knowledge of Cloud Computing on Azure
• Hunger and passion for learning new skills

Building the machine learning production System(or MLOps) is the biggest challenge most large companies currently have in making the transition to becoming an AI-driven organization. This position is an opportunity for an experienced, server-side developer to build expertise in this exciting new frontier. You will be part of a team deploying state-ofthe-art AI solutions for Fractal clients.

Responsibilities

As MLOps Engineer, you will work collaboratively with Data Scientists and Data engineers to deploy and operate advanced analytics machine learning models. You’ll help automate and streamline Model development and Model operations. You’ll build and maintain tools for deployment, monitoring, and operations. You’ll also troubleshoot and resolve issues in development, testing, and production environments.

• Enable Model tracking, model experimentation, Model automation
• Develop scalable ML pipelines
• Develop MLOps components in Machine learning development life cycle using Model Repository (either of): MLFlow, Kubeflow Model Registry
• Machine Learning Services (either of): Kubeflow, DataRobot, HopsWorks, Dataiku or any relevant ML E2E PaaS/SaaS
• Work across all phases of Model development life cycle to build MLOPS components
• Build the knowledge base required to deliver increasingly complex MLOPS projects on Azure
• Be an integral part of client business development and delivery engagements across multiple domains

Required Qualifications

• 5.5-9 years experience building production-quality software
• B.E/B.Tech/M.Tech in Computer Science or related technical degree OR equivalent
• Strong experience in System Integration, Application Development or Datawarehouse projects across technologies used in the enterprise space
• Expertise in MLOps, machine learning and docker
• Object-oriented languages (e.g. Python, PySpark, Java, C#, C++)
• Experience developing CI/CD components for production ready ML pipeline.
• Database programming using any flavors of SQL
• Knowledge of Git for Source code management
• Ability to collaborate effectively with highly technical resources in a fast-paced environment
• Ability to solve complex challenges/problems and rapidly deliver innovative solutions
• Team handling, problem solving, project management and communication skills & creative thinking
• Foundational Knowledge of Cloud Computing on Azure
• Hunger and passion for learning new skills

Responsibilities

• Design and implement advanced solutions utilizing Large Language Models (LLMs).
• Demonstrate self-driven initiative by taking ownership and creating end-to-end solutions.
• Conduct research and stay informed about the latest developments in generative AI and LLMs.
• Develop and maintain code libraries, tools, and frameworks to support generative AI development.
• Participate in code reviews and contribute to maintaining high code quality standards.
• Engage in the entire software development lifecycle, from design and testing to deployment and maintenance.
• Collaborate closely with cross-functional teams to align messaging, contribute to roadmaps, and integrate software into different repositories for core system compatibility.
• Possess strong analytical and problem-solving skills.
• Demonstrate excellent communication skills and the ability to work effectively in a team environment.

Primary Skills

• Generative AI: Proficiency with SaaS LLMs, including Lang chain, llama index, vector databases, Prompt engineering (COT, TOT, ReAct, agents). Experience with Azure OpenAI, Google Vertex AI, AWS Bedrock for text/audio/image/video modalities.
• Familiarity with Open-source LLMs, including tools like TensorFlow/Pytorch and Huggingface. Techniques such as quantization, LLM finetuning using PEFT, RLHF, data annotation workflow, and GPU utilization.
• Cloud: Hands-on experience with cloud platforms such as Azure, AWS, and GCP. Cloud certification is preferred.
• Application Development: Proficiency in Python, Docker, FastAPI/Django/Flask, and Git.
• Natural Language Processing (NLP): Hands-on experience in use case classification, topic modeling, Q&A and chatbots, search, Document AI, summarization, and content generation.
• Computer Vision and Audio: Hands-on experience in image classification, object detection, segmentation, image generation, audio, and video analysis.

Responsibilities

• Design and implement advanced solutions utilizing Large Language Models (LLMs).
• Demonstrate self-driven initiative by taking ownership and creating end-to-end solutions.
• Conduct research and stay informed about the latest developments in generative AI and LLMs.
• Develop and maintain code libraries, tools, and frameworks to support generative AI development.
• Participate in code reviews and contribute to maintaining high code quality standards.
• Engage in the entire software development lifecycle, from design and testing to deployment and maintenance.
• Collaborate closely with cross-functional teams to align messaging, contribute to roadmaps, and integrate software into different repositories for core system compatibility.
• Possess strong analytical and problem-solving skills.
• Demonstrate excellent communication skills and the ability to work effectively in a team environment.

Primary Skills

• Generative AI: Proficiency with SaaS LLMs, including Lang chain, llama index, vector databases, Prompt engineering (COT, TOT, ReAct, agents). Experience with Azure OpenAI, Google Vertex AI, AWS Bedrock for text/audio/image/video modalities.
• Familiarity with Open-source LLMs, including tools like TensorFlow/Pytorch and Huggingface. Techniques such as quantization, LLM finetuning using PEFT, RLHF, data annotation workflow, and GPU utilization.
• Cloud: Hands-on experience with cloud platforms such as Azure, AWS, and GCP. Cloud certification is preferred.
• Application Development: Proficiency in Python, Docker, FastAPI/Django/Flask, and Git.
• Natural Language Processing (NLP): Hands-on experience in use case classification, topic modeling, Q&A and chatbots, search, Document AI, summarization, and content generation.
• Computer Vision and Audio: Hands-on experience in image classification, object detection, segmentation, image generation, audio, and video analysis.