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4 Days ago

Machine Learning Engineer - L2

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Chennai, Tamil Nadu, India
Information Technology
Full-Time
Wissen Infotech

Overview

About Us

Established in the year 2000 in the US, we have global offices in the US, India, UK, Australia, Mexico, and Canada, with best-in-class infrastructure and development facilities spread across the globe. We are an end-to-end solution provider in Banking & Financial Services, Telecom, Healthcare, Manufacturing & Energy verticals and have successfully delivered $1 billion worth of projects for more than 20 Fortune 500 companies.

Position Name

Machine Learning Engineer  L2

Experience

2-5 Years

Location

Bangalore

Shift Timings

Client Specific

Job Description

"• Design and develop scalable machine learning models and deploy them in production 
environments.
• Build and implement agentic systems that can autonomously analyze tasks, process large 
volumes of unstructured data, and provide actionable insights.
• Collaborate with data scientists, software engineers, and domain experts to integrate AI 
capabilities into cutting-edge products and solutions.
• Develop deterministic and reliable AI systems to address real-world challenges.
• Create and optimize scalable, distributed ML pipelines.
• Perform data preprocessing, feature engineering, and model evaluation to ensure high 
performance and reliability.
• Stay abreast of advancements in AI technologies and incorporate them into business solutions.
• Participate in code reviews, contribute to system architecture discussions, and continuously 
enhance project workflows.
Required Skills and Qualifications
• Software Engineering Fundamentals: Strong foundation in algorithms, data structures, and 
scalable system design.
• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or related fields 
with a solid academic track record.
• Experience: 2-5 years of hands-on experience in building AI systems or machine learning 
applications.
• Agentic Systems: Proven experience in developing systems that utilize AI agents for automating 
complex workflows, analyzing unstructured data, and generating actionable outcomes.
• Programming: Proficiency in programming languages such as Python, Java, or Scala.
• AI Expertise: Experience with machine learning frameworks like TensorFlow, PyTorch, or 
Hugging Face libraries (e.g., for working with transformer-based models and LLMs).
• MLOps Knowledge (preferred): Familiarity with tools like MLflow, Kubeflow, Airflow, Docker, 
or Kubernetes.
• Cloud Platforms: Hands-on experience with AWS, Azure, or Google Cloud for deploying machine 
learning models.
• Big Data: Experience with data processing tools and platforms such as Apache Spark or Hadoop.
• Problem-Solving: Strong analytical and problem-solving skills, with the ability to create robust 
solutions for complex challenges.
• Collaboration and Communication: Excellent communication skills to articulate technical 
ideas effectively to both technical and non-technical stakeholders."

Competencies Key Skills

Python, Java, or Scala
TensorFlow, PyTorch, or Hugging Face libraries
MLflow, Kubeflow, Airflow, Docker
AWS, Azure, or Google Cloud

Soft Skills

Strong Communication Skills

Qualification

Any Bachelor's degree

Certifications

Key Responsibilities 

 

• Develop deterministic and reliable AI systems to address real-world challenges. 

• Create and optimize scalable, distributed ML pipelines. 

• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or related fields with a solid academic track record. 

• Experience: 2-5 years of hands-on experience in building AI systems or machine learning applications. 

• Agentic Systems: Proven experience in developing systems that utilize AI agents for automating complex workflows, analyzing unstructured data, and generating actionable outcomes. 

• Programming: Proficiency in programming languages such as Python, Java, or Scala. 

• AI Expertise: Experience with machine learning frameworks like TensorFlow, PyTorch, or Hugging Face libraries (e.g., for working with transformer-based models and LLMs).

 • MLOps Knowledge (preferred): Familiarity with tools like MLflow, Kubeflow, Airflow, Docker, or Kubernetes. Cloud Platforms: Hands-on experience with AWS, Azure, or Google Cloud for deploying machine learning models. 

 

If interested please share your update resume-supriya.gantlappa@wisseninfotech.com

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