Machine Learning (ML) Engineer

Summary role description:

Hiring a Machine Learning (ML) Engineer for a technology firm that develops AI-driven solutions for enterprises.

Company description:

Our client is a technology organization that develops software solutions for enterprise environments. Their platforms help businesses manage data, streamline processes, and improve operational efficiency. The solutions integrate with existing systems and support teams in monitoring and managing workflows. The company works with modern technologies to build scalable digital solutions and continues to explore new innovations in the technology space.

Role details:

• Title / Designation:Machine Learning (ML) Engineer
• Location:
• Experience: 3-6 years

Role & responsibilities:

• Build, train, and fine-tune LLMs, and evaluate their performance using techniques like SFT, LoRA/QLoRA, and RLHF.
• Optimize models for local or edge environments by improving speed and efficiency through quantization, pruning, and distillation.
• Deploy models into production on-premises or at the edge using frameworks such as PyTorch, ONNX, TensorRT, vLLM, or llama.cpp, and integrate them into applications via APIs and internal services.
• Design and maintain scalable training and inference pipelines to ensure reproducibility and efficiency.
• Monitor model performance in production, including accuracy, drift, latency, and resource utilization, and continuously optimize outcomes.
• Ensure models meet security, privacy, and compliance requirements, especially in restricted or offline environments.
• Collaborate with software engineers, infrastructure teams, and domain experts to deliver end-to-end AI solutions.
• Document model architectures, training processes, and deployment workflows for clarity and future use.

Candidate requirements:

• Strong hands-on experience with LLMs such as LLaMA, Mistral, Falcon, or similar open-weight models, along with proficiency in Python and frameworks like PyTorch or TensorFlow.
• Expertise in vector databases and retrieval systems (FAISS, Weaviate, Chroma, Pinecone, Milvus) and experience building RAG-based solutions.
• Experience developing and deploying models in local, on-premises, or resource-constrained environments, with a solid understanding of model optimization techniques like quantization, batching, and memory optimization.
• Hands-on experience with multi-agent AI systems (LangGraph, CrewAI, AutoGen, OpenAI Assistants API) and building autonomous or AI-driven workflows.
• Strong experience in end-to-end model development, working with business stakeholders to define KPIs and delivering multi-modal (text and image) or ensemble models.
• Familiarity with Linux, Docker, and basic cloud or on-prem infrastructure concepts.
• Experience with distributed training, multi-GPU systems, and handling large-scale models (10B+ parameters or multi-billion token datasets) is a plus.
• Knowledge of inference optimization tools such as vLLM, TensorRT-LLM, and ONNX, along with exposure to MLOps tools for model versioning and monitoring.
• Background in working with security-sensitive or regulated environments (such as finance, healthcare, or government) is preferred.

Selection Process:

• Two technical rounds
• One HR round

Recruiter Details:

Check Your Resume for Match

Upload your resume and our tool will compare it to the requirements for this job like recruiters do.