Senior Machine Learning Engineer
About Quantiphi
Quantiphi is an award-winning Data Science and Machine Learning Software and Services Company focused on helping organizations translate the big promise of Machine Learning technologies into quantifiable business impact. Founded on the belief that ML and AI are transformative technologies that will create the next quantum gain in customer experience, we are proud to be one of the five global launch partners for Google in Machine Learning and one of the three global launch partners for the Google Cloud Contact Center AI solution.
Our signature approach combines ground-breaking machine-learning research with disciplined cloud and data-engineering practices to create breakthrough impact at unprecedented speed. We believe in “Solving What Matters.”
Company Highlights
- Exponential Growth: 2.5x growth YoY since inception in 2013.
- Google Partner of the Year: Awarded Machine Learning Partner of the Year (2017, 2018), Social Impact Partner of the Year (2019), Data & Analytics Specialization Partner of the Year, and US Education Public Sector Partner of the Year (2020).
Role Overview
We are seeking a highly skilled and experienced Senior Machine Learning Engineer with deep specialization in the Google Cloud Platform (GCP) ecosystem and its advanced AI services.
In this critical role, you will support, optimize, and scale our deployment of the Google AI ecosystem—with a particular focus on Gemini Enterprise. You will act as a core technical anchor, working closely with clients and internal teams to architect robust ML solutions, troubleshoot complex pipelines, and champion MLOps best practices.
- Experience Level: 5–7 years
- Location: Remote / Hybrid (as applicable)
Key Responsibilities
- GCP AI Subject Matter Expertise: Serve as the go-to expert for GCP’s cutting-edge AI services, including Gemini Enterprise, AI Studio, and NotebookLM.
- Architecture & Optimization: Design and implement scalable, cost-effective ML solutions leveraging Vertex AI, Cloud Run, Cloud Functions, and core databases (BigQuery, Cloud SQL, Firestore).
- Gemini & Agent Development: Lead advanced implementations for Gemini Enterprise, encompassing prompt engineering, model fine-tuning, and building conversational AI agents using Agent Development Kits (ADKs).
- Troubleshooting & Operational Support: Diagnose complex technical issues across data pipelines and ML models. Manage incoming technical support requests via JIRA and dedicated MS Teams channels ("Gemini AI Support"), ensuring strict adherence to SLAs.
- Client Engagement: Act as a primary technical contact for internal teams and clients, translating complex technical queries into actionable advice.
- MLOps & Governance: Champion robust MLOps practices, ensuring rigorous security, compliance, and cost optimization within GCP.
- Mentorship & Documentation: Build and maintain thorough technical runbooks and documentation while mentoring junior engineers in GCP AI best practices.
Required Skills & Experience
- Education: Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field.
- Core Experience: 5+ years of professional experience as an ML or MLOps Engineer with a strong focus on cloud-native environments.
- Deep GCP Expertise: Direct experience with:
- Vertex AI (training, deployment, and monitoring).
- Cloud Run & Cloud Functions for serverless, event-driven ML inference.
- GCP Data Tier: BigQuery, Cloud SQL, and Firestore.
- Generative AI & LLMs: Proven hands-on experience with Google's Gemini family of models and building conversational agents via frameworks/ADKs.
- Software Engineering: Strong proficiency in Python and standard ML frameworks (TensorFlow, PyTorch, scikit-learn).
- Engineering Best Practices: Practical knowledge of CI/CD, Git, automated ML pipeline testing, and network/security fundamentals within GCP.
Preferred Qualifications
- Certifications: GCP Professional Machine Learning Engineer or Professional Cloud Architect.
- Specialized Tools: Experience with Google AI Studio and NotebookLM; familiarity with containerization (Docker, Kubernetes).
- Support Background: Prior experience in client-facing technical support roles handling SLAs.
Pay: ₹511,174.60 - ₹1,833,311.51 per year
Benefits:
- Health insurance
- Paid sick time
- Provident Fund
Work Location: Remote
