Deep Learning
Machine Learning (ML)
Data ScienceSenior Machine Learning Engineer
About the Role
We are looking for a Senior Machine Learning Engineer who can take business problems, design appropriate machine learning solutions, and make them work reliably in production environments.
This role is ideal for someone who not only understands machine learning models, but also knows when and how ML should be applied, what trade-offs to make, and how to 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.
What You’ll Be Doing
Building and Deploying ML Models
- Design, build, evaluate, deploy, and monitor machine learning models for real production use cases.
- Take ownership of how a problem is approached, including deciding whether ML is the right solution and what type of ML approach fits the problem.
- Ensure scalability, reliability, and efficiency of ML pipelines across cloud and on-prem environments.
- Work with data engineers to design and validate data pipelines that feed ML systems.
- Optimize solutions for accuracy, performance, cost, and maintainability, not just model metrics.
Leading and Architecting ML Solutions
- Lead a team of ML Engineers, providing technical direction, mentorship, and review of ML approaches.
- Architect ML solutions that integrate seamlessly with business applications and existing systems.
- Ensure models and solutions are explainable, auditable, and aligned with business goals.
- Drive best practices in MLOps, including CI/CD, model monitoring, retraining strategies, and operational readiness.
- Set clear standards for how ML problems are framed, solved, and delivered within the team.
Collaborating and Communicating
- Work closely with business stakeholders to understand problem statements, constraints, and success criteria.
- Translate business problems into clear ML objectives, inputs, and expected outputs.
- Collaborate with software engineers, data engineers, platform engineers, and product managers to integrate ML solutions into production systems.
- Present ML decisions, trade-offs, and outcomes to non-technical stakeholders in a simple and understandable way.
What We’re Looking For
Machine Learning Expertise
- Strong understanding of supervised and unsupervised learning, deep learning, NLP techniques, and large language models (LLMs).
- Experience choosing appropriate modeling approaches based on the problem, available data, and business constraints.
- Experience training, fine-tuning, and deploying ML and LLM models for real-world use cases.
- Proficiency in common ML frameworks such as TensorFlow, PyTorch, Scikit-learn, etc.
Production and Cloud Deployment
- Hands-on experience deploying and running ML systems in production environments on AWS, GCP, or Azure.
- Good understanding of MLOps practices, including CI/CD for ML models, monitoring, and retraining workflows.
- Experience with Docker, Kubernetes, or serverless architectures is a plus.
- Ability to think beyond deployment and consider operational reliability and long-term maintenance.
Data Handling
- 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 outcomes.
- Familiarity with distributed computing frameworks like Spark or Dask is a plus.
Leadership and Communication
- Ability to lead and mentor ML Engineers and work effectively across teams.
- Strong communication skills to explain ML 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 and AI, with a practical mindset toward adoption.
Experience Needed
- 6+ years of experience in machine learning engineering or related roles.
- Proven experience designing, selecting, and deploying ML solutions used in production.
- Experience managing ML systems after deployment, including monitoring and iteration.
- Proven track record of working in cross-functional teams and leading ML initiatives.
Shubham Vishwakarma
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Follow CutshortShubham Vishwakarma
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