Senior Machine Learning Engineer

As a Senior Machine Learning Engineer at Quantiphi, you will sit at the intersection of cutting-edge AI research and production-grade software engineering. You will be responsible for designing, building, and deploying scalable AI and Machine Learning systems, with a strong focus on the healthcare and life sciences domain.

This is a highly hands-on role where you are expected to spend 75% of your time writing production-grade code, designing system architectures, and implementing end-to-end pipelines. You will translate complex client requirements into robust technical designs, explain your architectural choices with deep technical reasoning, and mentor junior engineers to raise the team's overall engineering bar.

Role & Responsibilities:

• Hands-on Development: Write clean, modular, and highly optimized Python code. Build, train, fine-tune, and deploy statistical ML, Deep Learning, NLP, and Generative AI models.
• AI System Design & Representation: Design scalable, robust, and end-to-end AI architectures. You must be able to visually represent your system designs (using UML, block diagrams, or flowcharts) and clearly explain the reasoning behind your architectural choices and trade-offs.
• Generative AI & Agentic Systems: Build and optimize state-of-the-art Generative AI applications, advanced Retrieval-Augmented Generation (RAG) pipelines, and Agentic AI workflows.
• MLOps & Production Engineering: Set up and maintain production-grade MLOps pipelines including CI/CD, automated testing, model registry, monitoring, and retraining frameworks.
• Technical Leadership & Mentoring: Act as a technical anchor for the team. Guide and mentor junior engineers, perform rigorous code reviews, and champion software engineering best practices.
• Client Engagement & Reasoning: Lead technical discussions with clients. Clearly articulate complex technical concepts, updates, risks, and blockers to both technical and non-technical audiences. You must be able to justify your technical decisions with strong analytical reasoning.

Skills expectation:

• Must have:
• Experience: 4 to 7 years of professional experience in Machine Learning, Deep Learning, and Software Engineering.
• Strong Programming Foundations:
• Exceptional proficiency in Python, with a deep understanding of class-based, object-oriented, and modular coding standards.
• Strong proficiency in SQL for querying, processing, and analyzing complex, large-scale datasets.
• Comprehensive understanding of coding standards, Git-based version control, and CI/CD practices.
• Core ML & Deep Learning:
• Hands-on experience developing and deploying statistical ML models (regression, classification, clustering).
• Strong theoretical and practical understanding of Deep Learning architectures, particularly Transformers, CNNs, and RNNs.
• Experience in Natural Language Processing (NLP) including text embeddings, tokenization, and sequence-to-sequence models.
• Generative AI & Agentic AI:
• Practical experience designing and deploying Generative AI solutions and LLM-based applications.
• Hands-on implementation of advanced RAG (Retrieval-Augmented Generation) pipelines.
• Deep familiarity and hands-on experience with Vector Databases (e.g., Pinecone, Milvus, Chroma, Qdrant).
• Hands-on experience with Agentic AI Frameworks (e.g., LangChain, LlamaIndex, CrewAI, AutoGen) for multi-agent workflows and tool-use.
• AI System Design & Technical Reasoning:
• Proven ability to design scalable AI systems from scratch.
• Ability to visually diagram and represent architecture designs and explain technical trade-offs with deep, structured reasoning.
• Frameworks & Tools:
• Strong hands-on experience with PyTorch or TensorFlow.
• MLOps Basics:
• Experience with model tracking, monitoring, retraining, and production deployment strategies.
• Good to have:
• Domain Expertise: Previous experience working in the Healthcare & Life Sciences domain (familiarity with HIPAA, clinical data standards, or healthcare compliance is a huge plus).
• Databricks & PySpark:
• Experience using Databricks for model development, tracking, and collaboration.
• Hands-on experience with PySpark for distributed data processing and large-scale feature engineering.
• Agile Methodologies: Experience working in Agile/Scrum environments.