Senior Machine Learning Engineer

You won’t just build models—you’ll own how intelligence shows up in real products. This role sits at the intersection of data, engineering, and decision-making, where models aren’t experiments—they’re expected to perform, scale, and deliver measurable impact. Key Responsibilities Design, develop, and deploy scalable machine learning models across production environments Translate ambiguous business problems into structured ML solutions with clear success metrics Own end-to-end ML pipelines—from data ingestion and feature engineering to model deployment and monitoring Work closely with data engineers to ensure high-quality, reliable data pipelines Optimize models for performance, latency, and cost in real-world environments Continuously evaluate model performance and retrain pipelines based on drift or evolving data Collaborate with product and business teams to align ML outputs with decision-making needs Mentor junior engineers and bring engineering discipline into ML workflows Required Skills & Experience 5–9+ years of experience in machine learning, data science, or applied AI roles Strong programming skills in Python and experience with ML libraries (TensorFlow, PyTorch, Scikit-learn) Hands-on experience with model deployment (APIs, microservices, batch/real-time systems) Strong understanding of ML algorithms (supervised, unsupervised, deep learning, NLP, etc.) Experience working with large datasets and distributed systems (Spark, Hadoop, or similar) Familiarity with cloud platforms like AWS, GCP, or Azure Solid grasp of data structures, algorithms, and software engineering best practices Good to Have Experience with MLOps tools (MLflow, Kubeflow, Airflow, etc.) Exposure to GenAI / LLM-based applications Experience in A/B testing and experimentation frameworks Prior experience in scaling ML systems in production What Success Looks Like Models are not just accurate—but reliable and production-ready Clear impact on business metrics (revenue, efficiency, user experience) Reduced model downtime and faster iteration cycles Strong collaboration between engineering, data, and product teams