Job Summary:
We are seeking a highly skilled and experienced
AI/ML Engineer
to join our growing AI team. The ideal candidate will have a strong foundation in machine learning, deep learning, and data science, with hands-on experience in building scalable AI solutions using open-source tools and cloud infrastructure. You will work on cutting-edge projects involving generative AI, predictive modeling, and intelligent automation.
Key Responsibilities:
Design, build, and deploy advanced ML models for applications such as forecasting, anomaly detection, clustering, trend analysis, and pattern recognition.
Develop and optimize GenAI solutions leveraging models like
GPT-3.5/4/5, LLaMA 2, Falcon
, Gemini and apply prompt engineering best practices.
Build and maintain
basic Retrieval-Augmented Generation (RAG) pipelines
.
Process and analyze both
structured and unstructured data
from diverse sources.
Implement, test, and deploy ML models using
FastAPI, Flask, Docker
, and similar frameworks.
Conduct data preprocessing, feature engineering, and statistical analysis to prepare datasets for modeling.
Collaborate with cross-functional teams to integrate models into production systems hosted on
AWS
(EC2, S3, ECR).
Evaluate model performance using standard metrics and iterate on improvements.
Required Skills and Experience:
4+ years
of hands-on experience in AI/ML and Data Science with a strong grasp of open-source ML/DL tools.
Proficient in
Python
and data science libraries such as
NumPy, SciPy, Scikit-learn, Matplotlib
, and
CUDA
for GPU computing.
Strong experience in at least one of the following:
Time Series Analysis
Standard Machine Learning Algorithms
Deep Learning Architectures
Hands-on experience with
GenAI models
and prompt engineering techniques.
Working knowledge of
cloud platforms
, preferably
AWS
.
Familiarity with containerization and model deployment (Docker, FastAPI, Flask).
Solid understanding of
statistics
, model validation techniques, and evaluation metrics.
Preferred Expertise (One or More):
Proficiency with
object detection frameworks
such as
YOLO, Detectron, TFOD
, ideally in a distributed computing environment.
Deep knowledge of
deep learning architectures
like
CNN, RNN, Transformers (LSTM, ResNet, etc.)
.
Experience with
NLP models and frameworks
, including
BERT, ELMo, GPT-2, XLNet, T5, CRFs
, and ONNX.
Additional Qualities:
Strong analytical and problem-solving skills.
Ability to communicate technical concepts effectively.
Proactive decision-making and a strong sense of ownership.
