Role Overview
We are looking for a highly skilled Machine Learning Engineer who can design, build, and own end-to-end ML systems in production. This role requires a strong blend of machine learning expertise, backend engineering, and full-stack development, with a focus on building reliable, scalable platforms used by leadership and critical business functions.
Key Responsibilities
Design, develop, and maintain
end-to-end machine learning pipelines
, including data ingestion, training, evaluation, deployment, monitoring, and retraining.
Build and own
production-grade ML services
that are reliable, scalable, and fault-tolerant.
Architect and manage
async workflows and API-driven systems
for ML and data services.
Integrate ML solutions into
complex production environments
and distributed systems.
Design robust systems with a strong focus on
failure modes, observability, and guardrails
to ensure reliability.
Develop internal analytical tools used by
leadership and cross-functional teams
for decision-making.
Develop
interactive internal ML tools and dashboards using Streamlit
for model insights, monitoring, and experimentation.
Experience with cloud platforms (AWS, GCP, Azure).
Collaborate with data scientists and stakeholders to deliver impactful solutions.
Required Skills & Qualifications
Core Engineering Skills
Strong proficiency in
Python
,
SQL
, and building
RESTful APIs
Experience with
asynchronous programming and workflows
Solid understanding of
software engineering best practices
: Version control (
bitbucket
), Unit and integration testing, Code quality and maintainability
Machine Learning & MLOps
Build or integrate
data ingestion pipelines
(batch or streaming)
Experience in performing EDA and understand the analysis.
Proven experience managing the
full ML lifecycle
.
Hands-on experience with
MLOps practices and tools
:
Experiment tracking
Model versioning
Automated training and deployment pipelines
CI/CD for ML systems
Systems, Infrastructure & Orchestration
Experience building
scalable and reliable ML systems in production
Familiarity with:
Containerization
(Docker)
Orchestration platforms
(e.g., Kubernetes, Airflow, Prefect, Dagster)
Infrastructure as Code (IaC)
Experience with
distributed data processing systems
(e.g., Spark)
Understanding of
workflow orchestration and scheduling for ML pipelines
Full Stack Development
Experience developing
end-to-end applications
, including:
Backend pipelines and services
Frontend/UI components
Hands-on experience building
internal ML dashboards and tools using Streamlit
Ability to create
intuitive interfaces
for monitoring models, exploring data, and enabling stakeholder interaction
Required Qualifications
Master’s or PhD in Statistics, Data Science, Computer Science, or a related quantitative field.
3–4+ years of experience in data science or machine learning pipeline.
Strong expertise in statistical analysis and machine learning techniques.
Proficiency in:
Python (pandas, numpy, scikit-learn, statsmodels)
SQL
Data visualization tools
Experience working with large-scale operational datasets.
Preferred Qualifications
Experience working with Databricks or AzureML.
Familiarity with big data technologies (Spark, PySpark).
Experience working with cloud platforms (AWS, Azure, or GCP).
Knowledge of MLOps practices and model deployment frameworks.
All your information will be kept confidential according to EEO guidelines.
