Senior Machine Learning Engineer

Location: (Bangalore or Noida) 

The proliferation of machine log data has the potential to give organizations unprecedented real-time visibility into their infrastructure and operations. With this opportunity comes tremendous technical challenges around ingesting, managing, and understanding high-volume streams of heterogeneous data

As a Machine Learning Engineer, you’ll build the intelligence behind the next generation of agentic AI systems that reason over massive, heterogeneous log data. You’ll combine machine learning, prompt engineering, and rigorous evaluation to create autonomous AI agents that help organizations understand and act on their data in real time.

You’ll be part of a small, high-impact team shaping how AI agents understand complex machine data. This is an opportunity to work on cutting-edge LLM infrastructure and contribute to defining best practices in context engineering and AI observability.

Responsibilities

• Design, implement, and optimize agentic AI components, including context engineering, memory management, and prompts.
• Collaborate cross-functionally with product, data, and infrastructure teams to deliver end-to-end AI-powered insights.
• Operate autonomously in a fast-paced, ambiguous environment - defining scope, setting milestones, and driving outcomes.
• Ensure reliability, performance, and observability of deployed agents through rigorous testing and continuous improvement.
• Maintain a strong bias for action—delivering incremental, well-tested improvements that directly enhance customer experience.

Required Qualifications

• B.Tech, M.Tech, or Ph.D. in Computer Science, Data Science, or a related field.
• 4-6 years of hands-on industry experience with demonstrable ownership and delivery.
• Strong understanding of machine learning fundamentals, data pipelines, and model evaluation.
• Proficiency in Python and ML/data libraries (NumPy, pandas, scikit-learn); familiarity with JVM languages is a plus.
• Working knowledge of LLM core concepts, prompt design, and agentic design patterns.
• Strong communication skills and a passion for shaping emerging AI paradigms.

Desired Qualifications

• Prior experience building and deploying AI agents or LLM applications in production.
• Familiarity with modern agentic AI frameworks (e.g., LangGraph, LangChain, CrewAI).
• Experience with ML infrastructure and tooling (PyTorch, MLflow, Airflow, Docker, AWS).
• Exposure to LLM Ops - infrastructure optimization, observability, latency, and cost monitoring.

About Us

Sumo Logic, Inc. helps make the digital world secure, fast, and reliable by unifying critical security and operational data through its Intelligent Operations Platform. Built to address the increasing complexity of modern cybersecurity and cloud operations challenges, we empower digital teams to move from reaction to readiness—combining agentic AI-powered SIEM and log analytics into a single platform to detect, investigate, and resolve modern challenges. Customers around the world rely on Sumo Logic for trusted insights to protect against security threats, ensure reliability, and gain powerful insights into their digital environments. For more information, visit www.sumologic.com.

Sumo Logic Privacy Policy. Employees will be responsible for complying with applicable federal privacy laws and regulations, as well as organizational policies related to data protection.

WHO YOU ARE

You are a passionate Python and AI/ML Engineer minimum 4 years of hands-on experience building intelligent systems. You thrive in fast-paced environments, love solving complex problems with data and algorithms, and take pride in delivering AI solutions that create real business impact. You have experience with cutting-edge Generative AI, scalable ML pipelines, and production-grade systems and you're energized by working at the frontier of what AI can do.

WHAT YOU'LL DO

1. Design, develop, and deploy machine learning models and Generative AI solutions — including classification, clustering, summarization, search & ranking, and information extraction.
2. Own end-to-end ML pipelines — from data ingestion and preprocessing through model training, deployment, and production monitoring.
3. Collaborate with cross-functional teams to translate business requirements into AI-driven features — applying NLP, outlier detection, and deep learning techniques where applicable.
4. Build robust, scalable, and well-documented Python-based RESTful APIs to expose ML models and AI services in production environments.
5. Optimize database interactions and ensure efficient data storage and retrieval for AI applications across SQL and NoSQL systems.
6. Stay current with the latest advances in AI/ML — integrating emerging approaches such as RAG pipelines, LLM fine-tuning, and vector search into live products.

WHAT YOU'LL NEED 

Python
Strong hands-on proficiency for building, scripting, and deploying AI/ML systems.
NumPy · Pandas · FastAPI · Scikit-learn
Machine Learning
Applied expertise across supervised, unsupervised, and deep learning — classification, clustering, outlier detection.
PyTorch · TensorFlow · XGBoost · DBSCAN

Generative AI (2+ yrs)
Hands-on experience building with LLMs — prompt engineering, RAG pipelines, summarization, and AI-powered features.
LLMs · RAG · Prompt Eng. · Fine-tuning

NLP & Search / Ranking
Processes language and builds relevance engines — NER, embeddings, semantic search, and ranking models.
spaCy · BERT · FAISS · Elasticsearch

API Development
Designs and ships secure, well-documented RESTful APIs exposing ML models as production-ready services.
REST · FastAPI · OAuth2 · Swagger

Databases
Proficient in SQL and NoSQL stores for structured and unstructured data pipelines supporting AI workloads.
PostgreSQL · MongoDB · Vector DBs

GOOD TO HAVE

Cloud Platforms
Deploys and scales AI workloads on AWS, Azure, or GCP.
AWS · Azure

TypeScript / JavaScript
Frontend or full-stack exposure for building ML-powered product interfaces.
TypeScript · React · Node.js

MLOps
Manages the ML lifecycle — tracking, versioning, and pipeline automation.
MLflow · Kubeflow · CI/CD

Containerization & Orchestration
Packages and scales AI services using containers and cluster management.
Docker · Kubernetes

WHO YOU ARE

You are a passionate Python and AI/ML Engineer minimum 4 years of hands-on experience building intelligent systems. You thrive in fast-paced environments, love solving complex problems with data and algorithms, and take pride in delivering AI solutions that create real business impact. You have experience with cutting-edge Generative AI, scalable ML pipelines, and production-grade systems and you're energized by working at the frontier of what AI can do.

WHAT YOU'LL DO

1. Design, develop, and deploy machine learning models and Generative AI solutions — including classification, clustering, summarization, search & ranking, and information extraction.
2. Own end-to-end ML pipelines — from data ingestion and preprocessing through model training, deployment, and production monitoring.
3. Collaborate with cross-functional teams to translate business requirements into AI-driven features — applying NLP, outlier detection, and deep learning techniques where applicable.
4. Build robust, scalable, and well-documented Python-based RESTful APIs to expose ML models and AI services in production environments.
5. Optimize database interactions and ensure efficient data storage and retrieval for AI applications across SQL and NoSQL systems.
6. Stay current with the latest advances in AI/ML — integrating emerging approaches such as RAG pipelines, LLM fine-tuning, and vector search into live products.

WHAT YOU'LL NEED 

Python
Strong hands-on proficiency for building, scripting, and deploying AI/ML systems.
NumPy · Pandas · FastAPI · Scikit-learn
Machine Learning
Applied expertise across supervised, unsupervised, and deep learning — classification, clustering, outlier detection.
PyTorch · TensorFlow · XGBoost · DBSCAN

Generative AI (2+ yrs)
Hands-on experience building with LLMs — prompt engineering, RAG pipelines, summarization, and AI-powered features.
LLMs · RAG · Prompt Eng. · Fine-tuning

NLP & Search / Ranking
Processes language and builds relevance engines — NER, embeddings, semantic search, and ranking models.
spaCy · BERT · FAISS · Elasticsearch

API Development
Designs and ships secure, well-documented RESTful APIs exposing ML models as production-ready services.
REST · FastAPI · OAuth2 · Swagger

Databases
Proficient in SQL and NoSQL stores for structured and unstructured data pipelines supporting AI workloads.
PostgreSQL · MongoDB · Vector DBs

GOOD TO HAVE

Cloud Platforms
Deploys and scales AI workloads on AWS, Azure, or GCP.
AWS · Azure

TypeScript / JavaScript
Frontend or full-stack exposure for building ML-powered product interfaces.
TypeScript · React · Node.js

MLOps
Manages the ML lifecycle — tracking, versioning, and pipeline automation.
MLflow · Kubeflow · CI/CD

Containerization & Orchestration
Packages and scales AI services using containers and cluster management.
Docker · Kubernetes

Responsibilities:
○ Design and develop new software applications from scratch
○ Work with developers and other teams across the company to build new features and tools.
○ Add new product features using Python language.
○ Write code which is micro services complaint and can be reused.
○ Own problems end-to-end, thinking through everything from user experience, data models, scalability, operability and ongoing metrics.
○ Uphold our high engineering standards and bring consistency to the codebases, infrastructure, and processes you will encounter.
○ Test and debug code, Monitor and troubleshoot production issues
○ Collaborate with experts in product, design, and operations.
○ Proactive and prompt analysis and identification of functional bugs and performance issues

Requirements:
○ Enjoy being a generalist, working on both the frontend, backend, and everything in between to tackle problems and delight customers.
○ Thinking about tools and services, and writing high quality code.
○ We write code using Python, React and other languages like javascript, typescript, nodejs, shell scripts, AWS cloud services.
○ We use productivity enhancement tools like copilot, cursor etc.
○ Strong knowledge of Databases (either NoSql or RDBMS), DynamoDB and postgres preferred.
○ Strong experience with REST API development, Flask API, and JSON handling.
○ Hands-on experience with Python frameworks or libraries (any of the following is a plus): PySpark, Pandas, NumPy, asyncio, etc.
○ Exposure to ML / GenAI workflows is a strong advantage (model integration, vector DBs, embeddings pipelines, etc.)
○ Unit testing of REST API and server side components on Python
○ Working on CI/CD pipeline.
○ Server caching optimisations using memcache, etc
○ Authentication mechanisms like SAML SSO, MFA, etc
○ If you have worked on Integrations across various systems using APIs or messages that experience will be good to have.
○ Taking pride in working on projects to successful completion, involving a wide variety of technologies and systems.
○ Putting yourself in the shoes of your users, and creating an intuitive, delightful experience.
○ You should have understanding around Security for Web Applications and backend processes. Security is very important for us.
○ Holding yourself and others to a high bar when working with production systems.
○ Stitching many different services and processes together, even if you have not worked with them before.
○ Strong understanding of front-end and back-end technologies
○ Experience working in an agile development environment
○ Knowledge of nodejs is nice-to-have, however not a must.

Experience
○ 4-7 years experience in the IT/Technology industry.
○ 4+ years of experience in Python
○ 3+ years of experience in NoSql / RDBMS, DynamoDB and postgres preferred.
○ 3+ years of experience in working on code version source control tools like Git
○ Experience in sonarqube, use of copilot or similar productivity enhancement tools

Engineering at Enboarder:
○ We work in an environment where it matters to make the right design decisions the first time, and as a result, take on less technical debt.
○ We roll out code to production every 2 weeks.
○ We’re big fans of micro services architecture and are moving a lot of products towards micro services.
○ We like to experiment with new features and do not see failures as bad, but building blocks for future features.
○ We use AWS as our Infrastructure and continually improve. We provide ample opportunities to learn and use new AWS features.
○ Product is a highly collaborative initiative across multiple teams. Engineers are expected to understand and have product input, designing systems towards our long-term product vision.
○ Our customers are fine with fewer features, but are not ok with broken features.
○ People have a strong sense of ownership and accountability for what they’re building.
○ What we build today will be the foundation for other systems in the future.
○ We are very frank on discussing technical matters. If one disagrees with how things are being done, we encourage them to speak up and help us get to the truth faster.

*NOTE:* Hybrid work with 3 days in office.