Machine Learning Engineer

Machine Learning Engineer- 2 VACANCIES

Location:

EARLY JOINERS WILL BE PREFERRED

Compensation:

• Senior ML Engineer:

  Up to 40 LPA (68 years experience)

• ML Engineer Level 2:

  Up to 30 LPA (3–6 years experience)

Role 1: Senior Machine Learning Engineer

Position Overview:

Key Responsibilities:

• Architect, build, and optimize AI product features and ML systems.
  
• Lead the design, training, evaluation, deployment, and monitoring of ML models.
  
• Implement A/B testing and scalable inference APIs.
  
• Optimize GPU architectures, parallel training, and fine-tuning processes.
  
• Deploy and maintain LLM-based solutions customized to business needs.
  
• Ensure best practices in DevOps and LLMOps using Kubernetes, Docker, and orchestration frameworks.
  
• Mentor and guide junior ML engineers.
  

Technical Requirements:

• LLM & ML:

  Hugging Face OSS LLMs, GPT, Gemini, Claude, Mixtral, Llama

• LLMOps:

  MLFlow, Langchain, Langgraph, LangFlow, Langfuse, LlamaIndex, SageMaker, AWS Bedrock, Azure AI

• Databases:

  MongoDB, PostgreSQL, Pinecone, ChromDB

• Cloud:

  AWS, Azure

• DevOps:

  Kubernetes, Docker

• Languages:

  Python, SQL, JavaScript

• Certifications (Preferred):

  AWS Professional Solution Architect, AWS ML Specialty, Azure Solutions Architect Expert

What You’ll Do:

• Collaborate with product, data, and research teams to build scalable ML systems.
  
• Implement state-of-the-art techniques across NLP, Generative AI, RAG, and Transformer models.
  
• Build robust and automated data pipelines.
  
• Innovate through research and experimentation with emerging AI technologies.
  
• Present insights and influence key data-driven decisions.
  

What You Need to Succeed:

• Master’s degree or equivalent experience in Machine Learning or related field.
  

• 6–8 years

  of relevant industry experience in ML, software engineering, or data engineering.
• Strong expertise in Python, PyTorch, TensorFlow, and Scikit-learn.
  
• Proven track record in ML Ops and deploying models at scale.
  
• Excellent problem-solving skills and a passion for innovation.
  

Role 2: Machine Learning Engineer – Level 2

Position Overview:

Key Responsibilities:

• Design and implement AI product features.
  
• Maintain and optimize existing ML systems.
  
• Train, evaluate, deploy, and monitor ML models.
  
• Build ML pipelines for experiment, model, and feature management.
  
• Implement A/B testing and scalable inference APIs.
  
• Optimize GPU architectures and fine-tune models for improved performance.
  
• Deploy LLM-based solutions for specific use cases.
  
• Ensure LLMOps and DevOps best practices using Kubernetes and Docker.
  

Technical Requirements:

• LLM & ML:

  Hugging Face OSS LLMs, GPT, Gemini, Claude, Mixtral, Llama

• LLMOps:

  MLFlow, Langchain, Langgraph, LangFlow, Langfuse, LlamaIndex, SageMaker, AWS Bedrock, Azure AI

• Databases:

  MongoDB, PostgreSQL, Pinecone, ChromDB

• Cloud:

  AWS, Azure

• DevOps:

  Kubernetes, Docker

• Languages:

  Python, SQL, JavaScript

• Certifications (Bonus):

  AWS Professional Solution Architect, AWS ML Specialty, Azure Solutions Architect Expert

What You’ll Do:

• Work with cross-functional teams to design and implement ML solutions.
  
• Apply NLP, Generative AI, RAG, and Transformer-based models.
  
• Develop and monitor production-grade ML systems.
  
• Build scalable data and model pipelines.
  
• Stay updated with the latest ML and AI advancements.
  

What You Need to Succeed:

• Master’s degree or equivalent experience in Machine Learning.
  

• 3–6 years

  of experience in ML, software engineering, or data engineering.
• Proficiency in Python and JavaScript.
  
• Hands-on experience with ML Ops tools and frameworks.
  
• Strong problem-solving and analytical thinking skills.