Senior Machine Learning Engineer
Company Description Manthhan is dedicated to transforming procurement and supply chain operations through AI-driven automation. The organization focuses on reducing manual intervention in procurement, inventory management, and broader supply chain activities. By building intelligent, scalable, and domain-independent solutions, Manthhan aims to deliver greater efficiency and innovation across diverse industries. Team members work at the intersection of AI, automation, and operations, contributing directly to products that impact real-world business performance.
Role Summary:
We are looking for a hands-on Machine Learning Engineer to design, build, and deploy scalable AI solutions for enterprise procurement automation. You will be responsible for developing production-ready ML systems, implementing modern AI architectures, and delivering intelligent solutions using Machine Learning, Generative AI, OCR, NLP, and Computer Vision.
This role involves working across the complete ML lifecycle—from model development and evaluation to deployment, monitoring, optimization, and continuous improvement.
Key Responsibilities:
Design, develop, fine-tune, evaluate, and deploy Machine Learning and Generative AI models (LLMs, SLMs, VLMs).
Build AI-powered solutions using NLP, Computer Vision, OCR, and Intelligent Document Processing.
Design and implement RAG-based and non-RAG AI architectures for enterprise applications.
Develop Agentic AI workflows and AI orchestration pipelines.
Build scalable ML pipelines, model-serving infrastructure, and inference APIs.
Develop intelligent OCR solutions for document digitization, information extraction, and workflow automation.
Collaborate with Software Engineers, Product teams, and Domain Experts to integrate AI capabilities into enterprise applications.
Optimize models for latency, scalability, accuracy, and cost-efficient inference.
Implement model monitoring, evaluation pipelines, retraining strategies, and continuous learning workflows.
Apply MLOps best practices including model versioning, CI/CD, deployment automation, and monitoring.
Ensure deployed AI systems meet reliability, security, governance, and compliance requirements.
Prepare technical documentation, architecture documentation, and deployment guides.
Stay updated with emerging AI technologies and evaluate their applicability to business problems.
Requirements:
Bachelor's or master's degree in computer science, Artificial Intelligence, Machine Learning, or a related field.
5-10 years of experience in Machine Learning Engineering or AI solution development.
Strong understanding of Machine Learning, Deep Learning, Transformer architectures, and Generative AI.
Practical experience with RAG architectures, embeddings, vector databases, retrieval pipelines, and non-RAG approaches.
Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.
Hands-on experience with OCR technologies including Tesseract, PaddleOCR, EasyOCR, Azure Document Intelligence, AWS Textract, or Google Vision AI.
Experience working with NLP, Computer Vision, Deep Learning, and transformer-based models.
Familiarity with cloud platforms (AWS, Azure, or GCP), Docker, Kubernetes, and containerized deployments.
Good understanding of MLOps, model deployment, monitoring, CI/CD, and automation practices.
Strong software engineering fundamentals including APIs, data structures, algorithms, and distributed systems.
Preferred Skills:
Experience with LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or similar AI orchestration frameworks.
Experience with Vector Databases such as Pinecone, FAISS, ChromaDB, Milvus, or Weaviate.
Knowledge of FastAPI, REST APIs, and microservice architecture.
Experience with model optimization techniques such as LoRA, QLoRA, quantization, and efficient inference.
Exposure to document-heavy enterprise applications or regulated industries is an advantage.
What We're Looking For:
Strong engineering mindset with excellent analytical and problem-solving skills.
Passion for AI innovation and emerging technologies.
Experience building scalable, production-ready AI systems.
Ability to bridge Machine Learning research with software engineering best practices.
Excellent collaboration and communication skills.
Focus on scalability, performance, reliability, and business impact.