Lead Engineer, AVP

Role Overview: Join us as a Lead Engineer and support the technical delivery of a software engineering team. You will be responsible for developing solution design options, explaining pros and cons to key stakeholders for decision-making, and overseeing work quality to meet technical standards. This role offers an opportunity to hone your technical skills and advance your career at the associate vice president level. Key Responsibilities: - Support a team of developers and set technical direction for deliveries - Apply software engineering principles and methodologies to design, develop, test, and maintain applications and services - Monitor technical progress, safeguard functionality, scalability, and performance, and provide updates to stakeholders - Mentor the team in software languages and technical domains - Drive adoption of software engineering principles, processes, and best practices - Collaborate with engineers, architects, business analysts, and stakeholders to understand objectives, requirements, and options - Design and develop high-volume, high-performance, and high-availability applications using proven frameworks and technologies Qualifications Required: - Minimum eight years of experience in software engineering, software design, or database design and architecture - Experience in providing technical leadership and accountability for a software engineering team - Proficiency in test-driven development, automated test frameworks, mocking, stubbing, and unit testing tools - Knowledge of software delivery lifecycle phases and established development methodologies - Experience with Python packages like Pandas, SciKit Learn, PyTorch, TensorFlow, NLTK, Spacy, HuggingFace, LangChain, LangGraph, and CrewAI - Proficiency in database languages such as SQL, BigQuery, and NoSQL, as well as big data technologies and cloud environments like GCP, AWS, and Azure - Familiarity with AI and NLP approaches like BERT, GPT, Transfer Learning, Gen AI, Prompt Engg, and Vector DB - Experience in designing and building end-to-end machine learning-based pipelines and MLOPs lifecycle,