Machine Learning Engineer

About Client:

Our Client is a global IT services company headquartered in Southborough, Massachusetts, USA. Founded in 1996, with a revenue of $1.8B, with 35,000+ associates worldwide, specializes in digital engineering, and IT services company helping clients modernize their technology infrastructure, adopt cloud and AI solutions, and accelerate innovation. It partners with major firms in banking, healthcare, telecom, and media.

Our Client is known for combining deep industry expertise with agile development practices, enabling scalable and cost-effective digital transformation. The company operates in over 50 locations across more than 25 countries, has delivery centers in Asia, Europe, and North America and is backed by Baring Private Equity Asia.

Job Title: MLoPS Engineer

Key Skills: Machine Learning operations, Azure cloud platform, Azure ML, Azure DevOps, AI/ML, Python, TensorFlow, PyTorch, Keras, Git, and CI/CD tools, GenAI frameworks, LLM outputs, LLMOps

Job Locations: Gurugram

Experience: 6 - 8

Budget:

Education Qualification

Work Mode:

Employment Type:

Notice Period:

Interview Mode:

 6+ years of experience in machine learning operations or software/platform development.

 Strong experience with Azure ML, Azure DevOps, Blob Storage, and containerized model

deployments on Azure.

 Strong knowledge of programming languages commonly used in AI/ML, such as Python, R,

or C++.

 Experience with Azure cloud platform, machine learning services, and best practices.

Preferred Qualifications:

 Experience with machine learning frameworks such as TensorFlow, PyTorch, or Keras.

 Experience with version control systems, such as Git, and CI/CD tools, such as Jenkins,

GitLab CI/CD, or Azure DevOps.

 Knowledge of containerization technologies like Docker and Kubernetes, and infrastructure-

as-code tools such as Terraform or Azure Resource Manager (ARM) templates.

 Experience with Generative AI workflows, including prompt engineering, LLM fine-tuning, or

retrieval-augmented generation (RAG).

 Exposure to GenAI frameworks: LangChain, LlamaIndex, Hugging Face Transformers,

OpenAI API integration.

 Experience deploying optimized models on edge devices using ONNX Runtime, TensorRT,OpenVINO, or TFLite.

 Hands-on with monitoring LLM outputs, feedback loops, or LLMOps best practices.

 Familiarity with edge inference hardware like NVIDIA Jetson, Intel Movidius, or ARM Cortex-

A/NPU devices

pnomula@people-prime.com