Design, develop, train, and deploy production-grade
ML and GenAI models across
use cases including NLP, computer vision, and structured data modeling.
Leverage frameworks such as
TensorFlow
,
Keras
,
PyTorch
, and
LangChain
to build scalable deep learning and LLM-based solutions.
Develop and maintain
end-to-end ML pipelines
with reusable, modular components for data ingestion, feature engineering, model training, and deployment.
Implement and manage models on
cloud platforms
such as
AWS
,
GCP
, or
Azure
using services like
SageMaker
,
Vertex AI
, or
Azure ML
.
Apply
MLOps best practices
using tools like
MLflow
,
Kubeflow
,
Weights & Biases
,
Airflow
,
DVC
, and
Prefect
to ensure scalable and reliable ML delivery.
Incorporate
CI/CD pipelines
(using Jenkins, GitHub Actions, or similar) to automate testing, packaging, and deployment of ML workloads.
Containerize applications using
Docker
and orchestrate scalable deployments via
Kubernetes
.
Integrate LLMs with APIs and external systems using LangChain, Vector Databases (e.g., FAISS, Pinecone), and prompt engineering best practices.
Collaborate closely with
data engineers
to access, prepare, and transform large-scale structured and unstructured datasets for ML pipelines.
Build monitoring and retraining workflows to ensure models remain performant and robust in production.
Evaluate and integrate
third-party GenAI APIs or foundational models
where appropriate to accelerate delivery.
Maintain rigorous experiment tracking, hyper parameter tuning, and model versioning.
Champion industry standards and evolving practices in
ML lifecycle management
,
cloud-native AI architectures
, and responsible AI.
Work across global, multi-functional teams, including architects, principal engineers, and domain experts.
Essential Skills / Experience:
2-10 years
of hands-on experience in developing, training, and deploying
ML/DL/GenAI models
.
Strong programming expertise in
Python
with proficiency in
machine learning
,
data manipulation
, and
scripting
.
Demonstrated experience working with
Generative AI
models and
Large Language Models (LLMs)
such as GPT, LLaMA, Claude, or similar.
Hands-on experience with
deep learning frameworks
like
TensorFlow
,
Keras
, or
PyTorch
.
Experience in
LangChain
or similar frameworks for LLM-based app orchestration.
Proven ability to implement and scale
CI/CD pipelines
for ML workflows using tools like
Jenkins
,
GitHub
,
GitLab
, or
Bitbucket Pipelines
.
Familiarity with
containerization (Docker)
and orchestration tools like
Kubernetes
.
Experience working with
cloud platforms
(AWS, Azure, GCP) and relevant AI/ML services such as
SageMaker
,
Vertex AI
, or
Azure ML Studio
.
Knowledge of
MLOps tools
such as
MLflow
,
Kubeflow
,
DVC
,
Weights & Biases
,
Airflow
, and
Prefect
.
Strong understanding of
data engineering concepts
, including batch/streaming pipelines, data lakes, and real-time processing (e.g.,
Kafka
).
Solid grasp of
statistical modeling
,
machine learning algorithms
, and evaluation metrics.
Experience with
version control systems
(Git) and collaborative development workflows.
Ability to translate complex business needs into scalable ML architectures and systems.
Why Join Us?
Build and deploy cutting-edge
LLM and GenAI applications
that solve real-world problems
Collaborate with thought leaders across engineering, product, and data science
Work in a dynamic, cloud-native, and automation-driven AI environment
Accelerate your growth through certification programs and continuous learning
Be part of an innovation-first team that values openness, agility, and integrity
About Agilisium:
Agilisium, is an AWS technology Advanced Consulting Partner that enables companies to accelerate their "Data-to-Insights-Leap.
With $50+ million in annual revenue and over 30% year-over-year growth, Agilisium is one of the fastest-growing IT solution providers in Southern California.
Our most important asset? People.
* Talent management plays a vital role in our business strategy.
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