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