Senior AI/ML Engineer
10 hours ago
Job Summary:
We are looking for a results-driven
Senior AI/ML Engineer
to lead the development and deployment of scalable machine learning models and intelligent systems. You will be at the forefront of building AI solutions that solve high-value business problems, with full ownership from data preparation to model monitoring. This is a key role in a
hands-on, production-grade AI/ML team
, working with state-of-the-art tooling and infrastructure.
Key Responsibilities:
- Design and implement robust, end-to-end
ML models and AI pipelines
to solve real-world business challenges. - Build and maintain scalable
data pipelines
for structured and unstructured datasets — including data extraction, cleansing, feature engineering, and labeling. - Train and tune ML models using modern frameworks such as
TensorFlow, PyTorch
, and
scikit-learn
. - Deploy models to production using
MLOps tools
like
MLflow, Kubeflow
, or
Amazon SageMaker
. - Collaborate with Product, Engineering, and Data Science teams to
embed ML into customer-facing solutions
. - Monitor and optimise models for
performance, reliability, and drift detection
in live environments. - Conduct R&D on cutting-edge techniques in
LLMs, NLP, and computer vision
, and apply them to production use cases. - Build internal dashboards, logs, and traceability features to ensure robust
model governance
. - Write clean, reusable code and maintain thorough documentation of solutions and processes.
Required Skills & Qualifications:
- 8+ years of experience in
machine learning, AI engineering, or applied data science
roles. - Deep fluency in
Python
and core ML libraries: NumPy, Pandas, scikit-learn, TensorFlow, PyTorch. - Strong knowledge of
ML algorithms, statistical modeling
, and
deep learning architectures
(CNNs, RNNs, Transformers). - Experience with
cloud platforms
such as AWS, Azure, or GCP for training and deploying models. - Proficiency with
Docker, Kubernetes
, and DevOps tools for ML deployment. - Hands-on experience with
MLOps frameworks
like MLflow, Kubeflow, or SageMaker. - Familiarity with
CI/CD pipelines
,
model registries
, and version control best practices. - Ability to work with
large-scale, multimodal datasets
(text, time series, images, etc.). - Strong analytical, problem-solving, and collaboration skills.
Preferred Qualifications:
- Master's degree in computer science, AI, Data Science, or a related field.
- Experience building applications using
LLMs (e.g., GPT, BERT)
or working on
NLP and Computer Vision
problems. - Familiarity with
Big Data tools
such as Spark, Kafka, Databricks. - Contributions to
open-source ML/AI projects
or peer-reviewed publications. - Awareness of
AI ethics, data privacy regulations
, and responsible AI deployment practices.
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