Senior Machine Learning Engineer

While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.


If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!

Role: Senior Machine Learning Engineer

Exp: 4 -7Years

Location : Mumbai / Bangalore

• 4+ years of experience

• Hands on experience in Python

• Familiarity with enterprise software development practices, including version control, testing, and deployment

• In-depth understanding of state-of-the-art language models, such as GPT-3, BERT, or similar architectures.

• Hands-on experience with large language models, prompt engineering, and fine-tuning techniques

• Experience with data preprocessing, feature engineering, and model evaluation.

• Expertise in training and fine-tuning LLMs using popular frameworks such as TensorFlow, Pytorch, or Hugging Face Transformers.

• Work closely with our customer, product, business, engineering and research teams.

• Architect end-to-end generative AI solutions with a focus on LLMs and RAG workflows.

• Design and implement RAG-based workflows to enhance content generation and information

• Experience working with Firebase, AlloyDB, Pinecone and vector databases. 

• Design the core architecture of LLM agents, including the agent core, planning module, memory management, and integration with external tools and APIs

• Develop prompting strategies and fine-tune large language models to serve as the "brain" of the agent

• Integrate agents with external tools, databases, and APIs to provide access to necessary information and functionality

• Proven track record of successfully deploying and optimizing LLM models for inference in production environments.

• Excellent communication and collaboration skills with the ability to articulate complex technical concepts to both technical and non-technical stakeholders.

• Familiarity with containerisation technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) for scalable and efficient model deployment.

• Experience with GCP services like Vertex AI, BigQuery, Cloud Storage, Cloud run, Cloud Scheduler, Cloud function, Artifact registry, Load balancer and Secret manager