Senior Machine Learning Engineer

We are looking for a Senior Machine Learning Engineer who can take business problems, design appropriate machine learning solutions (including classical ML, deep learning, and LLM-based approaches), and make them work reliably in production environments.

This role is ideal for someone who not only understands machine learning models, but also has a strong foundation in data analysis and modeling, knows when and how ML or LLMs should be applied, and can take ownership from problem understanding to production deployment.

Beyond technical skills, we need someone who can lead a team of ML Engineers, design end-to-end ML solutions, and clearly communicate decisions and outcomes to both engineering teams and business stakeholders. If you enjoy solving real problems, making pragmatic decisions, and owning outcomes from idea to deployment, this role is for you.

• Design, build, evaluate, deploy, and monitor machine learning solutions, including traditional ML, deep learning, and LLM-powered systems, for real production use cases.
• Perform data analysis, experimentation, and validation to decide how a problem should be modeled.
• Decide when LLMs are appropriate versus classical ML or rule-based approaches.
• Take ownership of how a problem is approached, including deciding whether ML or LLMs are the right solution.
• Ensure scalability, reliability, and efficiency of ML and LLM pipelines across cloud and on-prem environments.
• Work with engineering teams to design and validate data pipelines that feed ML and LLM systems.
• Optimize solutions for accuracy, latency, cost, and maintainability, not just model metrics.

• Lead a team of ML Engineers, providing technical direction, mentorship, and review of ML and LLM approaches.
• Architect ML and LLM solutions that integrate seamlessly with business applications and existing systems.
• Ensure models and LLM-based systems are explainable, auditable, and aligned with business goals.
• Drive best practices in MLOps, including CI/CD, monitoring, retraining, and lifecycle management for both ML models and LLM-based systems.
• Set clear standards for how ML and LLM problems are framed, solved, and delivered within the team.

• Work closely with business stakeholders to understand problem statements, constraints, and success criteria.
• Translate business problems into clear ML/LLM objectives, inputs, and expected outputs.
• Collaborate with software engineers, data engineers, platform engineers, and product managers to integrate ML and LLM solutions into production systems.
• Present ML and LLM decisions, trade-offs, and outcomes to non-technical stakeholders in a simple and understandable way.

• Strong understanding of supervised and unsupervised learning, deep learning, NLP techniques, and large language models (LLMs).
• Strong foundation in data analysis, feature reasoning, experimentation, and evaluation.
• Experience deciding when to use LLMs versus classical ML or rule-based approaches.
• Experience training, fine-tuning, adapting, or integrating LLMs into real-world systems.
• Proficiency in common ML frameworks such as TensorFlow, PyTorch, Scikit-learn, etc.

• Hands-on experience deploying and running ML and LLM-based systems in production environments on AWS, GCP, or Azure.
• Good understanding of MLOps practices, including CI/CD, monitoring, retraining, and lifecycle management.
• Experience with Docker, Kubernetes, or serverless architectures is a plus.
• Ability to think beyond deployment and consider operational reliability, cost, and long-term maintenance, especially for LLM workloads.

• Strong programming skills in Python.
• Proficiency in SQL and working with large-scale datasets.
• Ability to reason about data quality, data limitations, and how they impact ML and LLM outcomes.
• Familiarity with distributed computing frameworks like Spark or Dask is a plus.

• Ability to lead and mentor ML Engineers and work effectively across teams.
• Strong communication skills to explain ML and LLM concepts, decisions, and limitations to business teams.
• Comfortable taking ownership and making decisions in ambiguous problem spaces.
• Passion for staying updated with advancements in ML, AI, and LLM technologies, with a practical mindset toward adoption.

• 6+ years of experience in machine learning engineering or related roles.
• Proven experience designing, selecting, and deploying ML and LLM solutions used in production.
• Experience managing ML and LLM systems after deployment, including monitoring and iteration.
• Proven track record of working in cross-functional teams and leading ML initiatives.