·
Design, develop, and deploy
machine learning and deep
learning models
for classification, regression, clustering,
forecasting, anomaly detection, and predictive analytics use cases.

·
Build and optimize
feature engineering pipelines
and data
preprocessing workflows for structured and unstructured datasets.

·
Perform
exploratory data analysis (EDA)
to identify
trends, patterns, and actionable insights from large-scale healthcare and
business datasets.

·
Develop and evaluate models using appropriate
metrics, cross-validation, and experimentation frameworks.

·
Implement NLP solutions for
text classification, entity
extraction, summarization, and information retrieval
.

·
Design and fine-tune
Large Language Models (LLMs)
and
transformer-based architectures for domain-specific applications.

·
Apply
transfer learning and prompt engineering
techniques to adapt foundation models to business and healthcare use cases.

·
Build AI-driven document intelligence solutions
using models such as
LayoutLM, Donut, and Table Transformers
for
key-value extraction and document understanding.

·
Support
MLOps and model deployment
, including
Docker-based packaging, cloud deployment, monitoring, and performance
optimization.

·
Collaborate with product, engineering, and
business teams to translate business problems into AI/ML and GenAI solutions.

·
Research and experiment with emerging AI
technologies, including
RAG (Retrieval-Augmented Generation), LLM alignment
techniques (RLHF, DPO, PPO, KTO), and model optimization methods
.

Requirements

·
9+ years of hands-on experience in AI/ML model
development and deployment.

·
Strong understanding of
machine
learning, deep learning, neural network architectures, training methodologies,
and optimization algorithms.

·
Proficiency in
Python
and AI/ML libraries such as
PyTorch,
TensorFlow, Scikit-Learn, Pandas, and NumPy.

·
Experience with
NLP
libraries such as
Hugging Face
Transformers, spaCy, and NLTK.

·
Hands-on experience with
LLM
fine-tuning, prompt engineering, and transformer-based models.

·
Experience with
SQL
and/or NoSQL databases and data manipulation at scale.

·
Knowledge of
cloud
platforms (AWS/Azure), Docker, and ML deployment workflows.

·
Strong analytical, problem-solving, and
communication skills, with the ability to explain complex AI concepts to
technical and non-technical stakeholders.

Preferred Skills:

·
Experience with
RAG architectures, vector databases, embeddings, and
semantic search
.

·
Familiarity with
LLM alignment techniques
such as
RLHF, DPO, PPO, and KTO
.

·
Experience with
computer vision or document AI models
for
OCR and document understanding.

·
Knowledge of
Airflow or other workflow orchestration tools
.

·
Experience working with
HIPAA, PHI, PII, or GDPR-compliant systems
.

·
Familiarity with
Linux, Git, Jupyter Notebooks, and Agile development
practices
.

·
Experience with
distributed computing and scalable AI infrastructure
is a strong advantage.