Principal Software Engineer

What you'll do

• Lead the end-to-end design, development, and production deployment of machine learning and Generative AI solutions.
• Act as the technical owner for large-scale ML initiatives, driving architecture decisions for scalability and reliability.
• Design and implement advanced agentic AI systems including multi-agent architectures and autonomous decision-making workflows.
• Establish evaluation, validation, and governance frameworks to ensure model safety, performance, and compliance.
• Implement MLOps, LLMOps, and DevOps best practices including CI/CD, monitoring, and cost optimization.
• Architect scalable cloud-based ML platforms using AWS, GCP, or Azure environments.
• Develop robust data architectures and pipelines to ensure data integrity for machine learning operations.
• Provide technical leadership, mentorship, and code reviews to junior and mid-level engineers.

What we're looking for

• Must have a Doctorate with 2 years experience, Master's with 4 years, Bachelor's with 6 years, or Associate's with 10 years.
• Deep expertise in machine learning, deep learning, and Generative AI including LLMs, transformers, and fine-tuning techniques.
• Proven track record of delivering production-grade ML/GenAI systems and designing scalable system architectures for high-throughput pipelines.
• Proficiency in MLOps/LLMOps ecosystems including MLflow, Kubeflow, Airflow, CI/CD, Docker, and Kubernetes.
• Demonstrated ability to leverage cloud platforms (AWS, Azure, or GCP) for data engineering and cost optimization.
• Advanced proficiency in Python and modern ML frameworks such as PyTorch, TensorFlow, Hugging Face, and LangChain.
• Experience with RAG architectures, vector databases, and agentic AI frameworks including orchestration and tool use.
• Ability to mentor and guide junior and mid-level engineers through design reviews and career guidance.