WALK-IN DRIVE- AI/ML Engineer - Agilisium

13/12/2025

SKILL :Python, Tensorflow,Gen AI (Must Have), Machine learning ,AWS, Agentic AI, Claude, Fast API (Good to have)

:

Introduction to the Role:

AI & Machine Learning Engineering

In this role, you will collaborate with cross-functional teams including data engineers, product managers, MLOps engineers, and architects to design and implement production-grade AI solutions across domains. If you're looking to work at the intersection of deep learning, GenAI, cloud computing, and MLOps this is the role for you.

Accountabilities:

• 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:

• 3–10 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.