Data Ops & ML Engineer

About the job

Experience: 4.00 + years Salary: Confidential (based on experience) Expected Notice Period: 7 Days Shift: (GMT+05:30) Asia/Kolkata (IST) Opportunity Type: Remote Placement Type: Full Time Contract for 3 Months(40 hrs a week/160 hrs a month) What do you need for this opportunity? Must have skills required: • Anthropic, OpenAI, Data Ops, MLOps, Google Cloud Platform, Machine Learning, PostgreSQL, Python LL is Looking for:This role is a hybrid Data Ops + ML Engineer position, responsible for building and scaling both the data ingestion foundation and AI/ML capabilities of the platform. The product analyses real customer conversations alongside CRM and campaign data to generate actionable insights for revenue teams. With a functional MVP already delivered, the next phase focuses on scaling the platform, strengthening observability, and establishing a robust foundation for multi-client onboarding. This is a high-ownership, ground-up role, setting standards for data and ML scale during a critical growth phase. About The Product • The platform enables marketing and commercial teams to identify customer patterns faster, operating at the intersection of B2B SaaS, LLMs, and sales enablement. • AI-native platform that analyses customer conversations along with sales and marketing data. • Modern cloud-native stack, built on Google Vertex AI and serverless workloads. • Current status: A functional MVP is live and moving into the scaling and enhancement phase. Responsibilities Data Ops • Extend and automate ingestion connectors for email, transcripts, and conversational tools. • Maintain standardized metadata and ensure traceability from data source to insight. • Define and evolve data models supporting RBAC, analytics, and AI-driven retrieval. • Own data quality validation, schema alignment, and error monitoring. ML / LLM Engineering • Enhance prompt design, error handling, and structured output quality. • Optimize token usage, latency, grounding strategies, and hallucination safeguards. • Define and implement evaluation metrics for insight quality and utilization. • Partner with engineering teams to support scalable model deployment and lifecycle management. Must-Have Experience & Skills Technical Requirements • Experience: 4+ years in hybrid data engineering and ML engineering teams preferred. • Languages: Strong proficiency in Python. • Cloud & Infrastructure: Hands-on experience with Google Cloud Platform, particularly Vertex AI and Cloud Functions. • Data Platform: Experience with PostgreSQL and strong data modeling skills. • ETL / Ingestion: Experience with Airbyte, Cloud Composer, or similar ingestion orchestration tools. • MLOps: Experience with API-driven LLM integrations (OpenAI, Anthropic, Vertex). Soft Skills & Behaviours • Strong ownership mindset with accountability for outcomes, not just tasks. • Bias for action, favoring pragmatism over perfection. • User-centric thinking, focusing on AI solutions that deliver clear, practical value. Success Criteria First 3–6 Months • Ingestion Foundation: Automated, reliable ingestion pipelines supporting multiple data formats and sources. • Data Quality: Robust tagging, validation, and metadata management enabling downstream AI use cases. • Insight Consistency: Prompts and model configurations that deliver repeatable, trusted insights. • Observability: Clear dashboards, alerting mechanisms, and data lineage controls in place. How to apply for this opportunity? • Step 1: Click On Apply! And Register or Login on our portal. • Step 2: Complete the Screening Form & Upload updated Resume • Step 3: Increase your chances to get shortlisted & meet the client for the Interview!

Requirements

• Python
• Google Cloud Platform
• PostgreSQL
• MLOps

Preferred Technologies

• Python
• Google Cloud Platform
• PostgreSQL
• MLOps

About the company

Uplers aims to simplify the hiring process, offering a platform that connects talented professionals with contractual opportunities. The company focuses on delivering high-quality experiences for candidates and employers alike.