Senior Python Developer

Role Overview: As a Lead Python Developer with ML expertise, your main responsibility will be to develop, test, and deploy high-quality Python-based applications and services. You will also lead and coordinate a small engineering team, mentor junior and mid-level engineers, drive ML/LLM project lifecycles, build scalable ML/GenAI pipelines, integrate MLOps/GenAIOps tooling, design and implement GenAI features, work with large-scale data processing technologies, create and manage CI/CD pipelines, and collaborate cross-functionally to align technical delivery with product/business goals. Key Responsibilities: - Lead and coordinate a small engineering team by assigning tasks, conducting code reviews, providing technical guidance, and ensuring timely delivery. - Mentor junior and mid-level engineers, fostering best practices in design, coding, testing, and deployment. - Drive end-to-end ML/LLM project lifecycles from development through production deployment. - Build and maintain scalable ML/GenAI pipelines and APIs. - Integrate and operationalize MLOps/GenAIOps tooling such as MLflow, Kubeflow, KServe, and SageMaker. - Design and implement GenAI features like prompt engineering, RAG pipelines, fine-tuning, and agentic workflows using LangChain/AutoGen. - Work with large-scale data processing technologies like PySpark, Kafka, Delta Lake, Hadoop, etc. - Create and manage CI/CD pipelines for model and application deployment in cloud or distributed environments. - Collaborate cross-functionally to align technical delivery with product/business goals. Qualification Required: - Expert-level Python programming skills with solid foundations in software design and data structures. - Proven experience deploying ML/LLM models into production. - Hands-on experience with MLOps/GenAIOps stacks like MLflow, Kubeflow, SageMaker, etc. - Familiarity with agentic AI frameworks such as LangChain, AutoGen, LangGraph, CrewAI, and their real-world application. - Experience with big data ecosystems such as PySpark, Kafka, HDFS, Delta Lake. - Comfortable with owning both development and deployment in cloud/distributed systems like AWS/GCP/Azure, Docker, Kubernetes. - Prior experience leading or mentoring engineering teams. Additional Company Details: The company values soft skills including a leadership mindset with the ability to motivate and grow engineers, strong problem-solving skills, decision-making abilities, and technical communication skills. Balancing hands-on delivery with team enablement is also emphasized. (Note: Good-to-have skills and qualifications sections have been omitted as they were not present in the provided job description) Role Overview: As a Lead Python Developer with ML expertise, your main responsibility will be to develop, test, and deploy high-quality Python-based applications and services. You will also lead and coordinate a small engineering team, mentor junior and mid-level engineers, drive ML/LLM project lifecycles, build scalable ML/GenAI pipelines, integrate MLOps/GenAIOps tooling, design and implement GenAI features, work with large-scale data processing technologies, create and manage CI/CD pipelines, and collaborate cross-functionally to align technical delivery with product/business goals. Key Responsibilities: - Lead and coordinate a small engineering team by assigning tasks, conducting code reviews, providing technical guidance, and ensuring timely delivery. - Mentor junior and mid-level engineers, fostering best practices in design, coding, testing, and deployment. - Drive end-to-end ML/LLM project lifecycles from development through production deployment. - Build and maintain scalable ML/GenAI pipelines and APIs. - Integrate and operationalize MLOps/GenAIOps tooling such as MLflow, Kubeflow, KServe, and SageMaker. - Design and implement GenAI features like prompt engineering, RAG pipelines, fine-tuning, and agentic workflows using LangChain/AutoGen. - Work with large-scale data processing technologies like PySpark, Kafka, Delta Lake, Hadoop, etc. - Create and manage CI/CD pipelines for model and application deployment in cloud or distributed environments. - Collaborate cross-functionally to align technical delivery with product/business goals. Qualification Required: - Expert-level Python programming skills with solid foundations in software design and data structures. - Proven experience deploying ML/LLM models into production. - Hands-on experience with MLOps/GenAIOps stacks like MLflow, Kubeflow, SageMaker, etc. - Familiarity with agentic AI frameworks such as LangChain, AutoGen, LangGraph, CrewAI, and their real-world application. - Experience with big data ecosystems such as PySpark, Kafka, HDFS, Delta Lake. - Comfortable with owning both development and deployment in cloud/distributed systems like AWS/GCP/Azure, Docker, Kubernetes. - Prior experience leading or mentoring engineering teams. Additional Company Details: The company values soft skills including a leadership mindset