Machine Learning Engineer

Machine Learning Engineer

Location : Lower Parel

Key Responsibilities

• Deploy, scale, and monitor LLMs/SLMs using containerized and hybrid deployment models.
  
• Build advanced context engineering systems (RAG pipelines, feature stores, context analysis).
  
• Optimize ML models using LoRA, QLoRA, pruning, and quantization for low-latency inference.
  
• Architect end-to-end MLOps pipelines including model versioning, automated testing, and monitoring.
  
• Develop high-throughput Python microservices (FastAPI) for ML model serving.
  
• Ensure code quality, performance testing, and mentor engineering team members.
  

Key Skills :

Core Engineering & MLOps (4+ Years)

• Strong Python development skills for ML systems
  
• Experience with FastAPI/Flask for ML model APIs
  
• Hands-on with MLOps tools (MLflow, Sagemaker, or equivalent)
  
• Understanding of microservices & event-driven architecture
  
• Proficiency in Docker and containerized ML deployments
  

LLM & ML Specialization (2+ Years Mandatory)

• Experience deploying and hosting LLMs/SLMs in production (vLLM/Ollama)
  
• Expertise in RAG, chunking, indexing, and other context engineering methods
  
• Experience with LoRA, QLoRA & quantization techniques
  
• Deep learning frameworks: PyTorch/TensorFlow
  
• Experience in core ML systems like recommendation engines, clustering, forecasting
  

Data & Deployment Skills

• Cloud & hybrid deployment on AWS/Azure/GCP
  
• Kafka/RabbitMQ for asynchronous model processing
  

Good to Have

• Knowledge of vector databases and advanced RAG tools
  
• Understanding of front-end integrations (React)
  
• Experience with semantic segmentation or NLP techniques
  
• Bachelors/Masters in Computer Science, Engineering, or related field
  
• 4+ years in software/ML engineering with 2+ years in MLOps/LLM systems
• If interested , kindly share your resume at poonam@r2rconsults.com