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.