BOLD is seeking a Technical Lead Specialist who can understand functional, technical & architectural views and has the ability to translate them into a low-level technical design for the developers. The successful candidate will be expected to work with the Engineering Manager and to interact with the technical architect of the program to achieve these goals. In addition, your role will be to mentor developers and contribute towards best practices and standards. 

Our SDETs work with Product Managers and Dev Teams to understand features and technical implementation and ensure our sites, products, and platforms perform to industry-beating quality. 

ABOUT THIS TEAM

BOLD Platform services team design, creates, manage and maintains the highly scalable and distributed services for the wider use of web and mobile product applications/portals/websites. The current list of services is based on Microservices architecture whose domain ranges from visitor tracking, User profiles, authentication, content and payments.

Platform team uses state of the art modern technologies like .Net framework, Python, Java, SQL Server, My SQL, Graph DB, SOLR, and a lot of other tools & technologies serving millions of users with hundreds of million transactions everyday through a scalable and fault tolerant architecture.

WHAT YOU’LL DO

• Excellent Object-Oriented coding practices, Refactoring, Clean Code, merging, etc. 
• Follow and recommend design principles and best practices for QA automation. 
• Able to develop and maintain test automation framework as per project requirements. 
• Understand automation test plans, product architecture, and domain in detail. 
• Solution-providing approach for technical queries and help/mentor test automation team. 
• Should be able to develop components that are extensible to test tools. 
• Hands-on experience in PoC solutions for automation and can demonstrate. 
• Excellent communication skills and ability to work independently with multiple tasks. 
• Test cutting-edge applications and products at the user level and code level 
• Be able to understand the software internals, debug issues, and write automated tests with scripting and automation tools 
• Assist in implementing and maintaining a Continuous Integration Solution 
• Contribute and/or own the development of high-quality test automation 
• Create and enhance backend automation tests and test tools 
• Ability to extend API automation framework to the next level 
• Work efficiently in a multi-project agile environment 
• Have to involve in performance, security, and scalability testing efforts 
• Write specialized testing tools for use in testing our software platform 
• Participate in test design and code reviews 
• Excellent debugging skills, measures, and analyses code for quality. 

 WHAT YOU’LL NEED

• At least 2 years of experience with search platforms such as Solr and modern search architectures. 
• Working knowledge of vector databases and semantic search solutions (e.g., Azure AI Search, Pinecone, or equivalent tools). 
• Strong understanding of Large Language Models (LLMs) and their application in search, relevance, and matching use cases. 
• Familiarity with ranking and re-ranking concepts and algorithms, and how they impact search quality and relevance 
• Have 5-7 years of experience in API Test Automation using Karate, Rest Assured, Cucumber, Java, BDD, or any other tool. 
• Strong knowledge of scripting languages like Core Java, C#, Python, etc.  
• Experience with source code controls like SVN, GIT, or CVS. 
• Exposure to Test and Defect Management Tools like JIRA, Trail, Zephyr, etc. 
• Experience with one or more database technology (SQL, NoSQL, Mongo, Solr) 

EXPERIENCE:  

• Lead SDET- 7 years+

#LI-SV1

We are looking for an experienced Database Architect to design and govern scalable, secure, and high-performing database solutions across enterprise applications. The ideal candidate should have strong expertise in database architecture, data modeling, performance tuning, cloud databases, security, and DevOps-enabled database deployments.

This role will work closely with application teams, DevOps, infrastructure, and business stakeholders to define the right database strategies and ensure robust, future-ready solutions.

Key Responsibilities

• Design logical, physical, and conceptual data models
• Define database architecture standards, schema design, naming conventions, and best practices
• Design scalable, multi-tenant, and high-availability database solutions
• Evaluate and recommend the right database technologies such as SQL Server, Azure SQL, PostgreSQL, DB2, and NoSQL platforms
• Optimize database performance through indexing, query tuning, partitioning, and execution plan analysis
• Design and implement security controls including access management, encryption, masking, and auditing
• Ensure compliance with standards such as GDPR, SOX, HIPAA, PCI, where applicable
• Define backup, restore, disaster recovery, and data integrity strategies
• Support CI/CD and automated database deployments using DevOps practices
• Collaborate with development and architecture teams to review and approve database designs
• Create ER diagrams, architecture documents, and runbooks
• Mentor developers / DBAs on database best practices

Required Skills

• Strong experience in SQL Server / Azure SQL Database
• Good experience in PostgreSQL and DB2
• Strong knowledge of data modeling, normalization, and database design
• Expertise in SQL performance tuning, indexing, query optimization, and execution plans
• Experience with cloud database architectures, preferably on Azure
• Understanding of high availability, failover, backup, and disaster recovery
• Knowledge of database security, access control, and compliance-driven design
• Experience with Git, CI/CD, Terraform / Bicep / ARM, Azure DevOps or GitHub Actions
• Good understanding of microservices, distributed systems, and integration-heavy applications
• Strong documentation, communication, and stakeholder management skills

Preferred

• Experience with NoSQL databases such as Cosmos DB / MongoDB / DynamoDB
• Exposure to regulated industries like Banking, Insurance, Healthcare, etc.
• Strong architectural thinking with ability to balance scalability, security, and business needs

Soft Skills

• Strong analytical and problem-solving ability
• Excellent communication and collaboration skills
• Ability to influence and challenge design decisions constructively
• Mentorship and leadership mindset

THIS IS 5 DAYS WORK FROM OFFICE OPPORTUNITY

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

Position Overview: 

We are looking for a lead cloud engineer who has got good experience working with atleast 5 Plus years of experience in AWS and in building cloud components and preferably comes from a development background. 

Roles and Responsibilities: 

• You will be responsible for the development of cloud agnostic control tower/landing zone platform.
• You will be responsible for the development of terraform based reusable cloud native infra as a code platform.
•  You will be responsible along with the team in rolling out continuous observability across different cloud providers.
•   You will be responsible along with the team in setting up best in class security posture on the cloud.
• You will be responsible for the development of tools and code bases that contribute to the development of platform under platform engineering initiatives for different teams .
•  You will be responsible for participating in Cloud and DevOps architecture connects.
• You will be responsible for mentoring junior resources with right coding best practices as an individual contributor.
•  You will be responsible along with the team for delivering infra platforms across different projects.
• You will be responsible along with the team for documenting processes and best practices to ensure knowledge sharing across the team.

Must Have Skills:

• Experience: Around 8 years of total experience, including at least 5 years of hands-on experience in cloud architecture and infrastructure engineering — specifically in building and implementing cloud environments, not merely deploying applications on the cloud.
• Expertise in DevOps & Cloud tools
• Hands-on experience with Infra as a code tools like terraform, ansible, puppet, chef, cloud formation , etc..
• Expertise in any Cloud (AWS) 
• Good understand of version control (Git, Gitlab, GitHub)
• Hands-on experience in Container Infrastructure (Docker, Kubernetes)
• Ability to define container-based environment topology following principles of designing a well-architected framework.
• Ensuring availability, performance, security and scalability of production system
• Hands on implementation experience in AWS control tower and at least 2 implementations
• Hand on experience with Artifact repositories (Nexus, JFrog Artifactory) ·
• Hands on experience with CI/CD tools on-premises/cloud (Jenkins, CircleCI, etc. )
• Hands on experience with Monitoring, Logging, and Security (CloudWatch, cloud trail, log analytics, hosted tools such as ELK, EFK, Splunk, Datadog, Prometheus,)
• Hands-on experience with scripting languages like Python, Ant, Bash, and Shell
• Hands-on experience in designing pipelines & pipelines as code.
• Hands-on experience in end-to-end deployment process & strategy
• Hands-on experience of AWS with a good understanding of computing, networks, storage, IAM, Security, and integration services
• Capability to write complex code - e.g., automation of recurring/mundane tasks,
• Good understand of OS administration (CPU, memory, network performance troubleshooting), also demonstrates strong troubleshooting skills ·
• Demonstrates HA/DR design on Cloud platform as per SLAs/RTO/RPO
• You should have solid understanding of Terraform, understanding of usage and creation of terraform modules
• Good exposure on AWS CLI and SDK like boto3, azure SDK, etc..s
• Good understanding of AWS networking (VPC, security group, routing, TGW and vpc peering
• Good understanding services like RDS – Mysql, MongoDB, Document DB, Postgres,
• Good understanding of Kubernetes self-managed clusters, Fargate, S3, NLB, ALB, API gateways, route 53, WAF, Advance shield, Lambda and KMS.

Qualification: 

• BE/B.Tech or equivalent degree in Computer Science or related field. 

Location: 

• Noida

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