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.

BOLD is seeking for QA professional who will work directly with the BI Development team to validate Business Intelligence solutions. He will build test strategy and test plans and test cases for ETL and Business Intelligence components. He will also validate SQL queries related to test cases and produce test summary reports. 

ABOUT THIS TEAM

BOLD Business Intelligence(BI) team is a centralized team responsible for managing all aspects of the organization's BI strategy, projects and systems. BI team enables business leaders to make data-driven decisions by providing reports and analysis. The team is responsible for developing and manage a latency-free credible enterprise data warehouse which is a data source for decision making and input to various functions of the organization like Product, Finance, Marketing, Customer Support etc.

BI team has four sub-components as Data analysis, ETL, Data Visualization and QA. It manages deliveries through Snowflake, Sisense and Microstrategy as main infrastructure solutions. Other technologies including Python, R, Airflow are also used in ETL, QA and data visualizations.

WHAT YOU’LL DO

• Work with Business Analysts, BI Developers to translate Business requirements into Test Cases  
• Responsible for validating the data sources, extraction of data, applying transformation logic, and loading the data in the target tables.  
• Designing, documenting and executing test plans, test harness, test scenarios/scripts & test cases for manual, automated & bug tracking tools. 

WHAT YOU’LL NEED

• Experience in Data Warehousing / BI Testing, using any ETL and Reporting Tool 
• Extensive experience in writing and troubleshooting SQL Queries using any of the Databases – Snowflake/ Redshift / SQL Server / Oracle 
• Exposure to Data Warehousing and Dimensional Modelling Concepts  
• Experience in understanding of ETL Source to Target Mapping Document  
• Experience in testing the code on any of the ETL Tools 
• Experience in Validating the Dashboards / Reports on any of the Reporting tools – Sisense / Tableau / SAP Business Objects / MicroStrategy  
• Hands-on experience and strong understanding of Software Development Life Cycle (SDLC) and Software Testing Life Cycle (STLC). 
• Good experience of Quality Assurance methodologies like Waterfall, V-Model, Agile, Scrum.  
• Well versed with writing detailed test cases for functional & non-functional requirements. 
• Experience on different types of testing that includes Black Box testing, Smoke testing, Functional testing, System Integration testing, End-to-End Testing, Regression testing & User Acceptance testing (UAT) & Involved in Load Testing, Performance Testing & Stress Testing. · Expertise in using TFS / JIRA / Excel for writing the Test Cases and tracking the  
• Exposure in scripting languages like Python to create automated Test Scripts or Automated tools like Query Surge will be an added advantage 
• An effective communicator with strong analytical abilities combined with skills to plan, implement & presentation of projects 

EXPERIENCE- 

• QA Engineer, BI: 2.5 years+
• Senior QA Engineer, BI: 4.5 years+

#LI-SV1

Key Responsibilities

As a AI Engineer/Senior AI Engineer and a key member of our engineering team in India, you will be responsible for designing, building, and owning the end-to-end lifecycle and release of our most critical AI-powered features within a microservices architecture. This role requires a balance of advanced technical depth in AI/ML and a strong sense of product ownership, ensuring our solutions deliver tangible user value at scale.

1. End-to-End AI System Ownership

• Design & Development: Design, build, and deploy production-grade AI systems, ranging from traditional Machine Learning models to modern LLM-powered features.
• Architecture & Deployment: Lead AI projects from concept to execution, including designing robust microservice architectures, modeling, rigorous evaluation, and secure deployment on AWS.
• Data Expertise: Work extensively with unstructured data, chunking, embeddings, vector databases (e.g., Pinecone, pgvector), and address complex real-world data quality challenges.
• Production Standards: Own the release process end-to-end, applying high engineering standards to ensure scalability, operability, and maintainability, aligning with Enboarder’s microservices philosophy.

1. Generative AI & LLM Focus

• LLM Integration: Evaluate, integrate, and operationalize LLM-based tools and APIs, including but not limited to Anthropic Claude and AWS Bedrock, to enhance product capabilities.
• Advanced Techniques: Apply expertise in prompt engineering, Retrieval-Augmented Generation (RAG), and advanced evaluation metrics for generative outputs.
• Agentic Systems: Explore and implement agentic AI frameworks, workflow orchestration (e.g., Langgraph, CrewAI, StrandsAgents), or multi-agent systems to automate complex user journeys.

1. Collaboration, Leadership, and Strategy

• Product Collaboration: Collaborate closely with product, design, and other engineering teams to translate business requirements into high-impact AI capabilities that drive measurable user and business outcomes.
• Technical Leadership: Stay updated on the latest AI/ML advancements, contribute to the company’s long-term technical strategy, and evaluate new technologies for adoption.
• Mentorship: Actively mentor and guide junior AI engineers, fostering a culture of technical excellence, clean code practices, and continuous experimentation.

Required Skills & Experience

• Total Experience: 6+ years of professional experience in the IT/Technology industry, with a substantial focus on building and deploying production systems.
• AI/ML Experience: 4+ years of direct experience in building and deploying ML or LLM-based systems in production environments, ideally within a high-traffic SaaS environment.
• Programming Mastery: Strong proficiency in Python and modern AI/ML libraries (e.g., PyTorch, TensorFlow, scikit-learn).
• Generative AI Tooling: Hands-on experience with LLM-based frameworks such as LangChain, AWS Bedrock, or comparable in-house/open-source solutions.
• Vector Databases: Practical experience working with vector databases (e.g., FAISS, Pinecone, PGVector) for scalable similarity search and RAG implementation.
• Cloud Architecture: Extensive experience deploying, monitoring, and operating applications on AWS, demonstrating deep awareness of scalability, security, and cost-optimization in a microservices environment.

Preferred Qualifications (Nice to Have)

• Familiarity with FastAPI and containerization technologies (Docker, Kubernetes).
• Experience with MLOps tools such as MLflow, Airflow, or similar workflow orchestration platforms.
• Ability to balance technical trade-offs between model performance, inference cost, and system complexity in AI deployments.
• Direct experience with Fine Tuning or Pre-training large language models (LLMs) on domain-specific data.

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

BOLD is seeking for Devops professional to own the production ML environments and MLOps infrastructure within infrastructure and data science teams. Handle day-to-day support, ad-hoc requests, and cross-team projects. Architect scalable MLOps pipelines using AWS services like SageMaker, EMR, and OpenSearch.

ABOUT THIS TEAM

Infrastructure team provides various services including automation, observability, cloud/server/network architectures, CICD, infrastructure as code, database administration, incident management, vendor management, security and compliance, and acquiring new skills. These services help to improve efficiency, reduce errors, and ensure fast and reliable application releases while maintaining security and compliance. Techops help teams monitor applications and infrastructure, create resilient infrastructure, identify and resolve IT service issues, manage vendors, and ensure cloud security and compliance. The team also focuses on continuous learning and implementing new technologies to provide better value to the organization.

WHAT YOU’LL DO

• Design and maintain end-to-end MLOps pipelines for data ingestion, feature engineering, model training, deployment, and monitoring. 
• Productionize ML/GenAI services, collaborating with data scientists on model serving and workflow optimization. 
• Implement monitoring, alerting, and observability to reduce MTTR and ensure production reliability. 
• Manage data/feature stores and search infrastructure for scalable ML inference. 
• Automate CI/CD for ML models and infrastructure with governance and security compliance. 
• Handle security patching, cost optimization, and 24x7 on-call rotations for critical services. 
• Coordinate cross-functionally with development, QA, ops, and data teams to innovate build/deployment processes.​ 

WHAT YOU’LL NEED

• 4.5+ years (Sr Engineer)/7+ years (Module Lead) in AWS MLOps with hands-on SageMaker (Pipelines, Model Registry, Studio), EMR, OpenSearch (kNN/vector search). 
• Python/Bash scripting for CI/CD, provisioning, monitoring of  FastAPI/Spring Boot web services; and Linux servers (Solr/OpenSearch). 
• AWS services (S3, DynamoDB, Lambda, Step Functions) with cost control, reporting; databases (MySQL, MongoDB). 
• Strong Linux and networking fundamentals;  
• Hands-on expertise in ML tools (MLFlow, Airflow, Metaflow, ONNX).​ 

EXPERIENCE-

• MLOps Sr. Engineer, DevOps - 4.5+ years 

#LI-SV1