Senior AI / ML Engineer

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.