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
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