Senior / Principal Machine Learning Engineer – AI & Production ML Systems

Company Overview

Role Overview

Key Responsibilities

• Design, develop, and deploy scalable machine learning systems and AI-powered applications in production environments.
• Build and optimize supervised, unsupervised, deep learning, and generative AI models serving real users at scale.
• Lead architecture decisions for ML infrastructure, feature stores, training pipelines, and inference systems
• Develop and maintain production-grade LLM applications, including RAG architectures, fine-tuning pipelines, prompt engineering frameworks, and evaluation systems.
• Establish and enhance MLOps practices including CI/CD, model versioning, monitoring, drift detection, and automated retraining.
• Partner closely with Product, Engineering, and Data teams to translate business challenges into scalable AI solutions.
• Improve model accuracy, latency, reliability, scalability, and cost efficiency across production systems.
• Mentor ML engineers and contribute to technical standards, best practices, and engineering excellence.
• Support strategic decisions related to AI infrastructure, cloud platforms, and enterprise data architecture.
• Utilize customer engagement and attribution platforms such as Adjust, MoEngage, and Firebase as data sources for advanced ML use cases including personalization, churn prediction, and campaign optimization.

Requirements

• 7–15+ years of experience in Machine Learning Engineering, AI Engineering, Software Engineering, or Data Science
• Proven track record building and operating production-grade AI systems beyond proof-of-concepts or research environments
• Strong Python development skills and software engineering fundamentals
• Experience owning end-to-end ML lifecycle including data pipelines, model training, deployment, monitoring, and optimization
• Hands-on experience with PyTorch, TensorFlow, XGBoost, LightGBM, and the Hugging Face ecosystem
• Strong expertise in LLMs, Generative AI, RAG architectures, vector databases, embeddings, and fine-tuning methodologies
• Experience with LangChain, LlamaIndex, OpenAI APIs, Anthropic APIs, and related AI frameworks
• Strong MLOps background including Docker, Kubernetes, MLflow, Airflow, Dagster, GitHub Actions, and model observability platforms
• Experience working with cloud platforms such as AWS, Azure, or GCP
• Strong data engineering experience with Databricks, Spark, PySpark, Delta Lake, Iceberg, and modern lakehouse architectures
• Ability to work directly with business stakeholders and influence technical direction
• Previous experience within high-growth technology companies, fintech organizations, scale-ups, or enterprise AI platforms is highly preferred