Senior Machine Learning Engineer

# **Join Our Journey at VuNet** **VuNet** is a pioneer in *Business Journey Observability*, leveraging Big Data and Machine Learning to transform digital experiences across the financial services. Our deep-tech platform provides end-to-end visibility into customer journeys — empowering proactive issue resolution, operational resilience, and superior user satisfaction. If you’ve ever used instant payment systems like UPI, chances are you’ve already experienced the power of our platform — we monitor over *28 billion digital transactions monthly*(that’s equal to watching 3 years of tik-tok videos)**,** *touching 400 million users* with leading banks and financial institutions*.* VuNet is Series B funded, part of NASSCOM DeepTech Club, awarded NASSCOM’s AI Gamechanger, recognized in Forbes DGEMS 200 and by several global analysts including Gartner, Omdia. We’re building a new category of observability purpose-built for complex digital journeys — across payments, lending, core banking and more — already powering some of the largest banks in India and MEA. # **Your Role:** **Senior Machine Learning Engineer** We are looking for a Senior Machine Learning Engineer who combines strong mathematical and statistical foundations with solid software engineering. This is not primarily a GenAI, prompt-engineering or AI-API integration role. You will build the underlying intelligence used by our observability platform: statistical models, machine-learning algorithms and analytical techniques that operate continuously on large volumes of time-series and operational data. *We are looking for someone who can reason from first principles, formulate a problem mathematically, evaluate alternative approaches, understand their assumptions and failure modes, implement the solution and take it all the way into reliable production.* You will work closely with platform engineering, SRE, product and domain teams, but will be expected to independently identify opportunities where better algorithms can materially improve the product. # **Roles & Responsibilities** ML Platform & Production Engineering - Enhance and productionize VuNet's ML/MLOps platform for building, orchestrating, deploying, validating and monitoring ML workloads. - Work with Temporal-based workflows and VuNet's ML frameworks for model execution, scheduling and lifecycle management. - Build mechanisms for model versioning, experimentation, benchmarking, validation, safe rollout and performance monitoring. - Engineer ML capabilities for continuous production workloads across thousands to tens of thousands of time series and monitored entities. - Design for high-throughput streaming data, distributed computation, low-latency inference, horizontal scalability and resource efficiency. Anomaly Detection & Behavioural Intelligence - Develop adaptive anomaly-detection algorithms across infrastructure, application, transaction and business time-series data. - Build techniques that automatically account for seasonality, trends, changing baselines, noise and workload patterns. - Explore change-point detection and distribution-shift techniques to identify meaningful behavioural changes. - Explore multivariate approaches where anomalies emerge from relationships among signals rather than from individual metrics. Forecasting & Predictive Operations - Develop forecasting algorithms for capacity planning, workload forecasting, resource exhaustion and proactive operations. - Build models capable of handling multiple seasonalities, incomplete/noisy telemetry and changing operational behaviour. - Quantify prediction uncertainty and identify leading indicators that provide early warning of degradation or saturation. Root Cause, Dependency and Incident Intelligence - Develop analytical and ML techniques that distinguish probable root causes from downstream symptoms. - Use service topology, dependencies, temporal relationships, behavioural correlation and statistical evidence for RCA. - Correlate alerts, anomalies and operational events into meaningful incidents using time, topology, entity relationships and behavioural similarity. - Develop techniques for event clustering, incident evolution, impact analysis, blast-radius identification and probable-cause ranking. - Explore causal inference and dependency-aware approaches where they provide measurable value. Algorithm Validation, Simulation & Quality - Build systematic benchmarking and validation frameworks for anomaly detection, forecasting, correlation and RCA algorithms. - Define evaluation measures including precision, recall, false-positive rate, detection delay, stability, forecasting error and operational usefulness. - Evaluate algorithms under seasonality, concept drift, noisy or missing telemetry, cold-start conditions and changing workloads. - Develop simulation and synthetic-data frameworks for realistic metrics, anomalies, incidents, failures and dependency scenarios. - Validate algorithms against labelled datasets, historical production data and controlled or simulated scenarios to prevent regressions. # **What You Bring** ### **Mandatory Skills** - Strong software engineering skills, particularly in Python. - Strong grounding in probability, statistics, statistical inference and machine learning. - Good understanding of time-series analysis including trends, seasonality, decomposition, forecasting and change detection. - Ability to reason about algorithms beyond library APIs: assumptions, trade-offs, computational complexity, failure modes and appropriate evaluation methods. - Experience building or materially adapting ML/statistical algorithms rather than only integrating pre-built AI services. - Experience taking algorithms from experimentation into reliable production systems. - Strong debugging and analytical problem-solving skills. - Understanding of distributed systems and high-volume data processing. - Ability to work with imperfect, noisy and evolving real-world datasets. - *High agency: identifies meaningful problems, forms hypotheses, prototypes solutions, validates them against data and drives successful approaches into production without waiting for detailed task definitions.* **Good to Have Skills** - Go experience for production services or high-performance components. - Experience with Apache Flink or similar streaming/data-processing frameworks. - Experience working with observability, monitoring, SRE, telemetry or AIOps systems. - Familiarity with metrics, logs, traces, service topology and operational event data. - Experience with Kafka or other streaming platforms. - Exposure to techniques such as Bayesian methods, clustering, dimensionality reduction, probabilistic models, causal inference or optimization. - Experience building large-scale simulation, benchmarking or synthetic-data frameworks. - Experience working on systems where false positives, model drift, latency and explainability have direct operational consequences. # **What We Offer** ### **Life at VuNet: Building the Future Together** At VuNet, we’re building a world-class observability platform, proudly **Made in India —** and we're just getting started. We’re a team of passionate problem-solvers who love tackling complex challenges. We learn fast, adapt quickly, and stay curious — especially when it comes to exploring and staying ahead of the curve with emerging technologies likeGen AI**.** More than just a tech company, VuNet is a place where collaboration, learning, and innovation are part of everyday life. We believe in working together, taking ownership, and growing as a team. If you’re looking to work on cutting-edge technology, make a real impact, and grow with a supportive team — you’ll feel right at home at **VuNet**. # **Benefits For You** - Health insurance coverage for you, your parents, and dependents. - Mental wellness and 1:1 counselling support. - A learning culture that promotes growth, innovation, and ownership. - Transparent, Inclusive