3 hours ago
Bengaluru, IndiaSenior
Responsibilities
- Own production ML capabilities end to end by translating business or modeling needs into pipelines or services and operating them in production.
- Build and evolve scalable training, model-refresh, feature and data-validation, batch, and real-time inference workflows.
- Design model lifecycle controls including experiment tracking, evaluation gates, promotion, rollback, lineage, reproducibility, and monitoring.
- Build platform primitives that help Data Science and AI product teams ship reliably, securely, and cost-effectively.
- Improve the performance, resilience, and observability of distributed ML workloads and model-serving systems.
- Partner with Data Science on evaluation design, data quality, model-health signals, and production debugging.
- Contribute to LLM and agent evaluation and serving infrastructure, including tracing, quality gates, and regression detection.
- Lead technical design for ambiguous projects, influence architecture across teams, mentor engineers, and communicate risks and operational status to stakeholders.
Requirements
- 6+ years of industry experience building and operating production machine-learning or data-intensive distributed systems with substantial end-to-end ownership.
- Strong Python engineering skills and experience designing maintainable, testable services and pipelines.
- Demonstrated MLOps experience with experiment tracking, model registries and versioning, CI/CD, reproducible training, validation, deployment strategies, rollback, and production monitoring.
- Experience with distributed data and ML infrastructure such as Spark, Ray, Databricks, Kubernetes, AWS, or equivalent platforms.
- Understanding of model-training and inference trade-offs involving data quality, feature engineering, evaluation, latency, throughput, cost, reliability, and model drift.
- Experience productionizing classical ML, deep-learning/NLP, embedding/retrieval, or LLM/agent workflows.
- Operational judgment and incident-response experience across data, model, infrastructure, and serving layers.
- Ability to translate ambiguous product and Data Science requirements into pragmatic technical plans and drive completion.
- Clear written and verbal communication with technical and non-technical partners.
- Preferred experience with MLflow, Databricks, Ray, Kubernetes, Triton, managed model serving, high-volume batch scoring, or low-latency online inference.
- Preferred experience with LLM or agent evaluation frameworks, tracing and observability, RAG, vector search, LangGraph, LangSmith, Amazon Bedrock, feature stores, data contracts, schema validation, or data-quality systems.
- Experience in B2B SaaS or high-scale data platforms is preferred.
Benefits
- Health coverage, paid parental leave, generous paid time off, paid holidays, quarterly self-care days, and stock options.
- Equipment and support for working and connecting with teams at home or in one of the company’s offices.
- Learning and development initiatives, including access to LinkedIn Learning.
- Quarterly wellness education sessions, wellness days, and ERG-hosted events.
Tech Stack
Categories
About 6sense
6sense builds a B2B revenue platform that uses intent data and AI to help marketing and sales identify in‑market accounts, run account‑based campaigns, and prioritize outreach. The company sells its software via SaaS subscriptions and integrates with common CRM and marketing automation tools. Founded in 2013 and headquartered in San Francisco, it is privately held and serves midmarket and enterprise B2B organizations.