Principal Software Engineer

What We Do

In this vital role, you will play a pivotal role in building and scaling our machine learning models from development to production. Your expertise in both machine learning and operations will be essential in creating efficient and reliable ML pipelines.

Roles & Responsibilities

• Lead the end-to-end design, development, and delivery of machine learning and Generative AI (GenAI) solutions, from problem framing to production deployment and business impact realization.
• Act as the technical owner for large-scale ML/GenAI initiatives, driving architecture decisions, scalability, reliability, and long-term maintainability.
• Define and institutionalize evaluation, validation, and governance frameworks for ML/GenAI systems, including model performance, prompt evaluation, safety guardrails, hallucination mitigation, and compliance.
• Partner directly with business stakeholders and product leaders to understand objectives, translate them into AI/ML solutions, and ensure measurable value delivery.
• Establish and enforce best practices in MLOps, LLMOps, and DevOps, including CI/CD, monitoring, observability, reproducibility, and cost optimization.
• Architect and oversee scalable cloud-based ML/GenAI platforms leveraging AWS, GCP, or Azure.
• Drive experimentation strategy, including A/B testing, prompt optimization, and iterative improvement of models and agent workflows.
• Provide technical leadership and mentorship to L4 and L5 engineers, including design reviews, code reviews, and career guidance.
• Lead cross-functional collaboration across data science, engineering, product, and business teams to deliver integrated AI solutions.
• Design, develop, and implement robust data architectures and platforms to support ML Operations.
• Ensure data integrity, accuracy, and consistency through rigorous quality checks and monitoring.

What We Expect Of You

• Doctorate degree and 2 years of experience OR Masters degree and 4 years of experience OR Bachelors degree and 6 years of experience OR Associates degree and 10 years of experience OR High school diploma / GED and 12 years of experience
• Deep expertise in machine learning, deep learning, and Generative AI (LLMs, transformers, embeddings, fine-tuning techniques).
• Proven track record of leading and delivering production-grade ML/GenAI systems end-to-end with measurable business impact with strong experience in designing scalable system architectures for ML and GenAI, including distributed systems and high-throughput pipelines.
• Expertise in MLOps/LLMOps systems (MLflow, Kubeflow, Airflow, CI/CD, Docker, Kubernetes).
• Strong system design, architecture, and problem-solving skills with the ability to operate independently and lead large initiatives.
• Demonstrated proficiency in leveraging cloud platforms (AWS, Azure, GCP) for data engineering solutions. Strong understanding of cloud architecture principles and cost optimization strategies.
• Proven ability to mentor and guide junior and mid-level engineers (L4/L5).

Good-to-Have Skills

• Degree in computer science, Statistics, and Data Science preferred. Masters degree and 6+ years experience OR Bachelors degree and 8+ years experience
• Cloud Computing certificate preferred
• Experience with big data ecosystems (Spark, Hadoop) and large-scale data processing
• Strong background in data engineering and building scalable data platforms
• Advanced proficiency in Python and modern ML/AI frameworks (PyTorch, TensorFlow, Hugging Face, LangChain or similar)
• Experience designing robust evaluation and validation systems, including automated evals, human-in-the-loop, safety testing, and monitoring frameworks
• Extensive experience with RAG architectures, vector databases, and knowledge-grounded systems
• Knowledge of advanced statistical modeling, experimentation design, and causal inference
• Experience with NLP, semantic search, embeddings, and vector search systems
• Familiarity with Responsible AI practices, including fairness, explainability, governance, and regulatory considerations
• Experience with cloud-native AI/ML services (AWS, Azure, GCP) and cost/performance optimization
• Experience with Databricks platform for enterprise-scale ML and GenAI workloads
• Exposure to advanced evaluation techniques, including red-teaming, adversarial testing, and synthetic data generation
• Experience with data modeling and performance tuning for both OLAP and OLTP databases
• Experience with Apache Spark, Apache Airflow, and Databricks platform

What You Can Expect Of Us

• A competitive benefits package, including a Retirement and Savings Plan with generous company contributions, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts
• A discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
• Stock-based long-term incentives
• Award-winning time-off plans
• Flexible work models where possible