Company Overview
Our client is a globally recognized financial technology and digital assets organization operating across multiple regulated markets worldwide. The company is investing heavily in artificial intelligence, machine learning, and large-scale data platforms to develop next-generation products that enhance customer experience, decision-making, automation, personalization, and business growth.
As part of its continued expansion, the organization is seeking a Senior / Principal Machine Learning Engineer to help architect and scale production-grade AI systems.
Role Overview
This is a highly technical, hands-on engineering role focused on designing, building, deploying, and optimizing AI and machine learning systems used in live production environments. The successful candidate will act as a technical leader, owning AI products from model development through deployment, monitoring, and continuous improvement.
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, Tensor
- Flow, XGBoost, Light
- GBM, and the Hugging Face ecosystem
- Strong expertise in LLMs, Generative AI, RAG architectures, vector databases, embeddings, and fine-tuning methodologies
- Experience with Lang
- Chain, Llama
- Index, 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 GCPStrong 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
