Machine Learning Engineer

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What You’ll Do

• Lead the design and deployment of end-to-end ML systems for enterprise applications, from experimentation to production.
• Apply large language models (LLMs) effectively by:
• Fine-tuning and evaluating domain-specific models
• Developing robust prompt engineering and orchestration strategies
• Optimizing inference pipelines for latency, throughput, and cost efficiency
• Write production-quality software with strong engineering rigor, including clean APIs and reliable systems, while collaborating closely with product engineers.
• Build high-reliability ML infrastructure, including training pipelines, model registries, observability, and CI/CD for ML.
• Ensure ML solutions meet enterprise standards for security, compliance, data privacy (e.g., SOC2, GDPR), explainability, and auditability.
• Develop evaluation and monitoring frameworks to measure accuracy, fairness, robustness, and drift in deployed models.
• Partner with product and GTM teams to identify high-value enterprise use cases for ML and translate them into scalable solutions.
• Collaborate directly with customer-facing teams to deliver high-impact enterprise projects.
• Mentor engineers and raise the bar for technical excellence across the organization.
• Influence technical strategy and help define the company’s long-term AI roadmap.

Who You’ll Be

• An experienced Python developer with strong knowledge of data structures, algorithms, and CI/CD pipelines.
• A Machine Learning professional skilled in data wrangling (SQL, pandas, NumPy), supervised and unsupervised learning, model evaluation, and feature engineering.
• Knowledgeable in Deep Learning frameworks such as PyTorch, with experience in neural networks and NLP models; exposure to generative and multi-modal models is a plus.
• Experienced in MLOps , including model serving, orchestration (Kubernetes), and experiment tracking, with the ability to design and deliver large-scale ML systems focused on cost optimization and reproducibility.
• Equipped with a solid foundation in linear algebra, probability, statistics, and calculus .
• An effective communicator who can translate technical concepts into clear business value and collaborate with non-technical stakeholders.
• A mentor and leader who provides guidance through code reviews, architectural decisions, and technical direction.

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