Senior Machine Learning Engineer - (15-30 joiner only)

Quantiphi; Bengaluru; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Quantiphi in Bengaluru seeks an experienced ML/AI engineer to build, train, fine-tune, and deploy statistical ML, DL, NLP, and Generative AI models. You will design scalable AI architectures and visually represent your designs, explaining trade-offs.

You will also lead MLOps pipelines, mentor junior engineers, and engage with clients, justifying technical choices with strong reasoning. Proficiency in Python, SQL, PyTorch/TensorFlow, and vector databases is expected.

Qualifications

• 4 to 7 years of professional experience in ML, DL, and software engineering.
• Proficiency in Python with object-oriented and modular coding standards.
• Strong SQL skills for large-scale data analysis.
• Solid Git/CI-CD practices and version control.
• Hands-on ML modeling (regression, classification) and DL architectures (Transformers, CNNs, RNNs).
• Experience in NLP: embeddings, tokenization, seq-to-seq models.

Responsibilities

• Hands-on development: write clean, optimized Python code for ML/DL/NLP and deploy models.
• AI system design: create scalable end-to-end architectures with diagrams and trade-offs.
• Generative AI & agentic systems: build RAG pipelines and LLM-based apps.
• MLOps & production engineering: CI/CD, monitoring, model registry, retraining pipelines.
• Technical leadership: mentor engineers, perform code reviews, promote best practices.
• Client engagement: lead technical discussions and justify decisions with reasoning.

Skills

Python

SQL

System Design

NLP

Generative AI

MLOps

Leadership

CI/CD

Tools

PyTorch

TensorFlow

Vector Databases

LangChain

Job description

Experience Level: 4 to 7 Years

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.
• 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.
• Technical Reasoning & Depth: Ability to explain complex technical decisions, architecture designs, and model choices under deep probing (explaining the "why", not just the "how").
• Visual Communication: Comfort in using visual tools to present and explain complex system integrations.
• Client-Facing Presence: A pleasant, charismatic, and articulate communication style. Ability to lead technical discussions with clients, address risks, and resolve blockers.
• Mentorship: Passion for guiding junior engineers and fostering a culture of continuous learning and high engineering standards.

What is in it for you:

• Cutting-Edge Stack: Work with the latest 2026 AI/ML innovations, including Agentic AI, LLMs, and advanced MLOps.
• End-to-End Ownership: Own your deliverables from initial concept and system architecture to production deployment.
• Sponsored Certifications: Opportunities to get sponsored certifications across major cloud providers (GCP, AWS, Azure) and tools (Databricks, Tableau, etc.).
• Accelerated Growth: Join a fast-growing, award-winning AI-first organization with a highly collaborative and energetic work culture.