BOLD is seeking for newest addition to our existing Data Science team, you’ll combine your analytical skills and business knowledge to deliver key insights to our Product and Management teams. You’ll help drive product development using our vast user data sets to enhance our current products and to help us find inferences and relationships in this data to build new products – More towards the role required

ABOUT THIS TEAM

The Data Science department at BOLD is responsible for discovering patterns and trends in datasets to get insights, creating predictive algorithms and data models, improving the quality of data or product offerings by utilizing machine learning techniques, distributing suggestions to other teams and top management, and using data tools such as Python and SQL.

The Data Science team actively collaborates with other vertical teams such as Engineering, Portals, BI, Product, Legal. Most of the projects are focused around problems that require a mix of natural language processing and machine learning. Some of the active projects are resume parsing, ranking, summary generation, data quality and scoring, content generation, job recommendations, and conversion analysis. Apart from the business initiatives, the team also explores state of the art methods and keeps them upto to date with technology.

WHAT YOU’LL DO 

• Demonstrate ability to work on data science projects involving predictive modelling, NLP, statistical analysis, vector space modelling, machine learning etc. 
• Leverage our rich data sets of user data to perform research, develop models and create data products with our Development & Product teams 
• Develop novel and scalable data systems in cooperation with our system architects that leverage datasets using machine learning techniques to enhance the user experience 

WHAT YOU’LL NEED 

• Knowledge and experience using statistical and machine learning algorithms including regression, instance-based learning, decision trees, Bayesian statistics, clustering, neural networks, deep learning, ensemble methods 
• Expert knowledge in Python.  
• Practical LLM/RAG experience for search quality  such as  query understanding, semantic retrieval, reranker design .  
• Experience with frameworks that optimizes prompt engineering (langChain/crewai/etc) 
• Experience with Solr/OpenSearch/Elasticsearch … 
• Experience with embedding & vector search  
• Should have done one or more projects involving fine-tuning with LLMs 
• Experience in feature selection, building and optimising classifiers. 
• Experience working with backend technologies such as Flask/ Gunicorn etc. 
• Experience on working with open source library such Spacy,NLTK,Gensim etc  
• Experience on working with deep-learning library such as Tensor flow, Pytorch etc  
• Experience with software stack components including common programming languages, back-end technologies, database modelling, continuous integration, services oriented architecture, software testability etc. 
• Be a keen learner and enthusiastic about developing software.    

EXPERIENCE- 

• Software Engineer- 2.5 years+
• Senior Software Engineer-4.5 years+

#LI-SV1

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

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 an Solutions Engineer, you will actively contribute to, architect for, and lead the company in the design and development of new infrastructure automation projects. You will be instrumental in helping us solve, by architecture and implementation, complex hybrid infrastructure problems among corporate’s robust inventory of networking systems, applications, and cloud infrastructure. You will work closely with a team of highly skilled individuals to deliver on high impact engagements at Sumo Logic, reporting directly to the Global Head of Infrastructure and Data and engaging on projects under the guidance of the Lead Solution Architect..

You are a strong solutions engineer who is passionate about scalable, resilient, secure, and cost-efficient infrastructure solutions. You care about producing clean, elegant, maintainable, robust, well-tested code; you do this as a member of a team, helping the group come up with a better solution than you would as an individual. Ideally, you have experience with performance, scalability, and reliability issues of 24x7 uptime systems and solutions.

Responsibilities:

• Designing and implementing complex and well-architected enterprise infrastructure and data solutions
• Work to improve existing systems, solutions, and process through architectural reviews and design enhancement initiatives
• Experience in devops methodologies and practices as applied to infrastructure, cloud, identity, integration and network solutions
• Improve systems to provide strict compliance adherence, security guardrails, and cost-optimization
• Contribute to the re-architecting of systems, solutions, and processes for more streamlined, scalable, and resilient implementations
• Contribute to well-architected, end-to-end provisioning solutions to a growing enterprise organization across multiple environments and types of infrastructure
• Build strong partnerships and engage with other teams at Sumo Logic to create solutions that enable and benefit all parties and increased derived value

Requirements:

• 3+ years of industry experience with a proven track record of ownership and delivery
• Experience developing scalable, secure, and resilient infrastructure architectures and implementations
• Experience in observability and cost-optimization in application, server, and cloud (AWS preferred) settings
• Experience working with on-premise (Fortinet) network devices on a system administration and configuration level
• Experience with DNS (CloudFlare and Route53 preferred) as well as VPN management (FortiClient, ZScaler, and Netskope preferred)
• Experience with Identity Providers (Okta preferred)
• Experience with Certificate Management (Digicert and AWS ACM preferred) and authentication 
• Hands-on infrastructure as code experience (Terraform preferred) as well as CI/CD pipelines (Scalr and GitHub Actions preferred)
• Experience with writing automations and system integrations (Python preferred)
• Strong experience with AWS
• Excellent verbal and written communication skills
• Experience implementing robust and well-architected designs for enterprise scale systems
• Experience and comfort with an on-call schedule for enterprise systems during daylight hours regional time

Desirable:

• Experience in the following specific technologies is a plus:
• ADP
• Azure
• GCP
• Experience with Scalr
• Experience with AI tooling such as Boomi, Databricks (AI portions), AWS Bedrock, Gemini, and Claude
• Experience with Databricks as a whole
• CCNA, CCNP, or equivalent network certifications
• You should have a passion for continuous learning and deep technological curiosity
• Agile development experience and a familiarity with Jira, sprints, and pointing

About Us:

Sumo Logic, Inc., empowers the people who power modern, digital business.  Sumo Logic enables customers to deliver reliable and secure cloud-native applications through its SaaS analytics platform. The Sumo Logic Continuous Intelligence Platform™ helps practitioners and developers ensure application reliability, secure, and protect against modern security threats, and gain insights into their cloud infrastructures. Customers worldwide rely on Sumo Logic to get powerful real-time analytics and insights across observability and security solutions for their cloud-native applications. For more information, visit www.sumologic.com.

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 a Devops professional who owns 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

• Architect and productionize microservices with advanced DevOps pipelines , partnering with data science/ML teams to optimize model serving on Kubernetes platforms with service mesh.
• Design comprehensive observability frameworks to minimize MTTR and ensure24x7platform reliability.
• Engineer secure, automated CI/CD workflows with governance and compliance controls
• Lead 24x7 on-call engineering, CDN & security hardening and cost optimization strategies IAAS & PAAS in Cloud
• Manage infrastructure operations, production deployments, and hybrid cloud environments.
• Drive cross-functional collaboration with development, QA, operations, and data teams across global time zones to scale OpenSearch/EMR platforms and platform operations

WHAT YOU’LL NEED

• 4.5+ years (Sr. Engineer) / 7+ years (Module Lead)AWS/DevOps production experience
• Scripting: Python/Bash/Groovy/Java(CI/CD, EKS, FastAPI/Spring Boot)
• CI/CD: Jenkins (declarative pipelines, Groovy),Spinnaker, CodePipeline /CodeBuild, Gradle, Maven, Artifactory, GitHub Actions
• IaC:Terraform, AWS CloudFormation
• Containers: EKS + Istio/ Gloo/ Karpenter, ECS, Docker, Buildah
• AWS: VPC/IAM/GuardDuty/AWS Config, S3/ DynamoDB /Lambda /SageMaker /EMR, cost optimization
• Observability: Splunk, CloudWatch, Prometheus/Grafana, New Relic, Azure Monitor
• GitHub: Actions automation, branch/PR governance
• DNS management (NS1/Cloudflare/GoDaddy)

EXPERIENCE-

• Sr. Engineer Devops AWS (Automation)- 4.5 years+

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