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 : Machine Learning Engineer
Experience : 1-3 Years
Location: Bangalore

Notice Period : 0-15 Days

Key Responsibilities

• Implement and deploy generative AI models (e.g., large language models, diffusion models) based on architectural guidance and research specifications.

• Build and optimize agentic AI systems - autonomous agents that perceive, reason, and act effectively in complex environments using frameworks like LangChain, CrewAI, and Google Agent Development Kit (ADK).

• Work with Model Context Protocol (MCP) to enable seamless integration between AI agents and external tools and data sources.

• Develop production-ready implementations by translating insights from recent ML/AI research papers into functional code.

• Apply core NLP concepts including tokenization, embeddings, attention mechanisms, and transformer architectures in practical applications.

• Collaborate with senior engineers and data scientists to build scalable ML pipelines and deploy models into production environments.

• Develop and integrate APIs using FastAPI for model serving and application development.

• Implement prompt engineering techniques to optimize model performance and output quality.

• Conduct model evaluation and experimentation to assess performance, accuracy, and efficiency of ML systems.

• Maintain comprehensive technical documentation for implementations, experiments, and deployment processes.

• Learn from existing codebases and contribute to improving ML engineering best practices within the team.

• Take ownership of assigned tasks and ensure timely delivery of high-quality implementations.

• Support research efforts by implementing proof-of-concepts and contributing to internal knowledge sharing.

• Stay updated with the latest developments in generative AI, agents, and ML technologies through continuous learning.

Qualifications

• Bachelor's degree in Computer Science, Machine Learning, AI, or related fields with 1.5 to 3 years of industry experience in Machine Learning engineering or related roles.

• Strong programming skills in Python with hands-on experience in implementing ML solutions.

• Proficiency with deep learning frameworks such as PyTorch, TensorFlow, or similar.

• Experience working with Large Language Models (LLMs) and generative AI technologies.

• Understanding of Natural Language Processing (NLP) fundamentals and transformer architectures.

• Familiarity with Hugging Face Transformers library for model implementation and fine-tuning.

• Experience with ML libraries including Scikit-learn, Pandas, and NumPy for data processing and model development.

• Hands-on experience with LLM APIs such as OpenAI API and Anthropic API.

• Knowledge of agent development frameworks like LangChain or CrewAI.

• Experience with version control systems (Git, GitLab) and collaborative development workflows.

• Demonstrated ability to read research papers and translate them into working implementations.

• Strong problem-solving skills with attention to detail and code quality.

• Excellent communication and collaboration skills for working in cross-functional teams.

Preferred Skills

• Experience with advanced deep learning techniques including fine-tuning, transfer learning, and model optimization.

• Knowledge of  reinforcement learning, diffusion models, or multimodal learning architectures.

• Knowledge of transformer architecture variants (BERT, GPT, T5) and their practical applications.

• Familiarity with model evaluation methodologies, experimentation frameworks, and A/B testing for ML models.

• Experience with prompt engineering techniques and systematic prompt optimization strategies.

• Hands-on experience building and deploying generative AI applications using Large Language Models.

• Exposure to Model Context Protocol (MCP) or similar agent-tool integration frameworks.

• Experience with Google Agent Development Kit (ADK) or similar agentic frameworks.

• Proficiency with agent orchestration frameworks like LangChain or CrewAI for building complex AI workflows.

• Knowledge of MLOps practices including model versioning, logging and tracing, and monitoring in production.

• Familiarity with Google Cloud Platform (GCP) services including Vertex AI, Cloud Functions, BigQuery, and Cloud Storage.

• Experience with API development and microservices architecture using FastAPI.

• Experience with containerization using Docker and understanding of deployment workflows.

• Knowledge of container orchestration tools like Kubernetes.

• Familiarity with other cloud platforms (AWS, Azure) and their ML services.

• Strong technical documentation skills for code, experiments, and system architectures.