Job Description

Responsibilities:





• Design, develop, and deploy machine learning models to address various business challenges.


• Collaborate with cross-functional teams to understand business requirements and translate

them into scalable and efficient ML solutions.


• Implement and optimize ML models for performance, scalability, and reliability.


• Conduct thorough data analysis to prepare datasets for training and validation of ML models.


• Experiment with fine-tune hyperparameters to improve model accuracy and performance.


• Develop, maintain, and improve the ML pipeline, ensuring reproducibility and scalability of ML

experiments.


• Implement MLOps practices to streamline the deployment, monitoring, and maintenance of

ML models in production.


• Work with AWS cloud platforms for ML model deployment and scaling, including but not

limited to AWS SageMaker, AWS Lambda, Amazon S3, Amazon EC2, and Amazon EKS.


• Ensure models adhere to best practices in terms of security, ethics, and compliance.


• Collaborate with data engineers to build and maintain the necessary data infrastructure for

ML projects.


• Stay updated with the latest advancements in machine learning, MLOps, and large language

models.


• Document model architectures, data pipelines, and MLOps processes thoroughly.


• Provide mentorship and guidance to junior ML engineers and data scientists.





Qualifications:


• Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related

field.


• 5-8 years of experience in building and deploying machine learning models.


• Proven experience with natural language processing (NLP) and large language models (LLMs),

including OpenAI GPT, Mistral, and LLAMA, with successful execution of related projects.


• Familiarity with Langchain framework and its application in developing ML solutions.


• Experience in developing ML models using algorithms such as linear regression, random forest,

decision tree, and other advanced techniques.


• Proficiency in supervised, unsupervised, and reinforcement learning models.


• Strong programming skills in Python and proficiency with ML libraries such as TensorFlow,

PyTorch, and scikit-learn.


• Solid understanding of MLOps principles and experience with tools such as MLflow, Kubeflow,

or similar.


• Experience with AWS cloud platforms for deploying and scaling ML models, including AWS

SageMaker, AWS Lambda, Amazon S3, Amazon EC2, and Amazon EKS.


• Proficiency in data analysis and manipulation using tools like pandas, NumPy, and SQL.


• Experience with data versioning and experiment tracking tools.


• Knowledge of ethical considerations and best practices in AI/ML.


• Experience with real-time data processing and streamlining ML workflows.


• Strong problem-solving skills and the ability to work with complex datasets.


• Excellent communication and teamwork abilities.


• Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).


• Knowledge of software engineering best practices including version control, testing, and

CI/CD.


• Experience in working within an Agile development environment.


• Strong attention to detail and the ability to document processes clearly.