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