CoffeeBeans

As an L3 Data Scientist, you’ll work alongside experienced engineers and data scientists to solve real-world problems using machine learning (ML) and generative AI (GenAI). Beyond classical data science tasks, you’ll contribute to building and fine-tuning large language model (LLM)– based applications, such as chatbots, copilots, and automation workflows.

Key Responsibilities

• Collaborate with business stakeholders to translate problem statements into data science tasks.
• Perform data collection, cleaning, feature engineering, and exploratory data analysis (EDA).
• Build and evaluate ML models using Python and libraries such as scikit-learn and XGBoost.
• Support the development of LLM-powered workflows like RAG (Retrieval-Augmented Generation), prompt engineering, and fine-tuning for use cases including summarization, Q&A, and task automation.
• Contribute to GenAI application development using frameworks like LangChain, OpenAI APIs, or similar ecosystems.
• Work with engineers to integrate models into applications, build/test APIs, and monitor performance post-deployment.
• Maintain reproducible notebooks, pipelines, and documentation for ML and LLM experiments.
• Stay updated on advancements in ML, NLP, and GenAI, and share insights with the team.

Required Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
• 6–9 years of experience in data science, ML, or AI (projects and internships included).
• Proficiency in Python with experience in libraries like pandas, NumPy, scikit-learn, and matplotlib.
• Basic exposure to LLMs (e.g., OpenAI, Cohere, Mistral, Hugging Face) or a strong interest with the ability to learn quickly.
• Familiarity with SQL and structured data handling.
• Understanding of NLP fundamentals and vector-based retrieval techniques (a plus).
• Strong communication, problem-solving skills, and a proactive attitude.

Nice-to-Have (Not Mandatory)

• Experience with GenAI prototyping using LangChain, LlamaIndex, or similar frameworks.
• Knowledge of REST APIs and model integration into backend systems.
• Familiarity with cloud platforms (AWS/GCP/Azure), Docker, or Git.

As an L1/L2 Data Scientist, you’ll work alongside experienced engineers and data scientists to solve real-world problems using machine learning (ML) and generative AI (GenAI). Beyond classical data science tasks, you’ll contribute to building and fine-tuning large language model (LLM)– based applications, such as chatbots, copilots, and automation workflows.

Key Responsibilities

• Collaborate with business stakeholders to translate problem statements into data science tasks.
• Perform data collection, cleaning, feature engineering, and exploratory data analysis (EDA).
• Build and evaluate ML models using Python and libraries such as scikit-learn and XGBoost.
• Support the development of LLM-powered workflows like RAG (Retrieval-Augmented Generation), prompt engineering, and fine-tuning for use cases including summarization, Q&A, and task automation.
• Contribute to GenAI application development using frameworks like LangChain, OpenAI APIs, or similar ecosystems.
• Work with engineers to integrate models into applications, build/test APIs, and monitor performance post-deployment.
• Maintain reproducible notebooks, pipelines, and documentation for ML and LLM experiments.
• Stay updated on advancements in ML, NLP, and GenAI, and share insights with the team.

Required Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
• 2.5–5 years of experience in data science, ML, or AI (projects and internships included).
• Proficiency in Python with experience in libraries like pandas, NumPy, scikit-learn, and matplotlib.
• Basic exposure to LLMs (e.g., OpenAI, Cohere, Mistral, Hugging Face) or strong interest with the ability to learn quickly.
• Familiarity with SQL and structured data handling.
• Understanding of NLP fundamentals and vector-based retrieval techniques (a plus).
• Strong communication, problem-solving skills, and a proactive attitude.

Nice-to-Have (Not Mandatory)

• Experience with GenAI prototyping using LangChain, LlamaIndex, or similar frameworks.
• Knowledge of REST APIs and model integration into backend systems.
• Familiarity with cloud platforms (AWS/GCP/Azure), Docker, or Git.

Focus Areas:

• Build applications and solutions that process and analyze large-scale data.
• Develop data-driven applications and analytical tools.
• Implement business logic, algorithms, and backend services.
• Design and build APIs for secure and efficient data exchange.

Key Responsibilities:

• Develop and maintain data processing applications using Apache Spark and Hadoop.
• Write MapReduce jobs and complex data transformation logic.
• Implement machine learning models and analytics solutions for business use cases.
• Optimize code for performance and scalability; perform debugging and troubleshooting.
• Work hands-on with Databricks for data engineering and analysis.
• Design and manage Airflow DAGs for orchestration and automation.
• Integrate and maintain CI/CD pipelines (preferably using Jenkins).

Primary Skills & Qualifications:

• Strong programming skills in Scala and Python.
• Expertise in Apache Spark for large-scale data processing.
• Solid understanding of data structures and algorithms.
• Proven experience in application development and software engineering best practices.
• Experience working in agile and collaborative environments.

We are looking for experienced Data Engineers who can independently build, optimize, and manage scalable data pipelines and platforms.

In this role, you’ll:

• Work closely with clients and internal teams to deliver robust data solutions powering analytics, AI/ML, and operational systems.
• Mentor junior engineers and bring engineering discipline into our data engagements.

Key Responsibilities

• Design, build, and optimize large-scale, distributed data pipelines for both batch and streaming use cases.
• Implement scalable data models, warehouses/lakehouses, and data lakes to support analytics and decision-making.
• Collaborate with stakeholders to translate business requirements into technical solutions.
• Drive performance tuning, monitoring, and reliability of data pipelines.
• Write clean, modular, production-ready code with proper documentation and testing.
• Contribute to architectural discussions, tool evaluations, and platform setup.
• Mentor junior engineers and participate in code/design reviews.

Must-Have Skills

• Strong programming skills in Python and advanced SQL expertise.
• Deep understanding of ETL/ELT, data modeling (OLTP & OLAP), warehousing, and stream processing.
• Hands-on with distributed data processing frameworks (Apache Spark, Flink, or similar).
• Experience with orchestration tools like Airflow (or similar).
• Familiarity with CI/CD pipelines and Git.
• Ability to debug, optimize, and scale data pipelines in production.

Good to Have

• Experience with cloud platforms (AWS preferred; GCP/Azure also welcome).
• Exposure to Databricks, dbt, or similar platforms.
• Understanding of data governance, quality frameworks, and observability.
• Certifications (e.g., AWS Data Analytics, Solutions Architect, or Databricks).

Other Expectations

• Comfortable working in fast-paced, client-facing environments.
• Strong analytical and problem-solving skills with attention to detail.
• Ability to adapt across tools, stacks, and business domains.
• Willingness to travel within India for short/medium-term client engagements, as needed.

We are seeking an experienced Lead DevOps Engineer with deep expertise in Kubernetes infrastructure design and implementation. This role requires someone who can architect, build, and manage enterprise-grade Kubernetes clusters from the ground up. You’ll lead modernization initiatives, shape infrastructure strategy, and work with cutting-edge cloud-native technologies.

🚀 Key Responsibilities

Infrastructure Design & Implementation

• Architect and design enterprise-grade Kubernetes clusters across AWS, Azure, and GCP.
• Build production-ready Kubernetes infrastructure with HA, scalability, and security best practices.
• Implement Infrastructure as Code with Terraform, Helm, and GitOps workflows.
• Set up monitoring, logging, and observability for Kubernetes workloads.
• Design and execute backup and disaster recovery strategies for containerized applications.

Leadership & Team Management

• Lead a team of 3–4 DevOps engineers, providing technical mentorship.
• Drive best practices in containerization, orchestration, and cloud-native development.
• Collaborate with development teams to optimize deployment strategies.
• Conduct code reviews and maintain infrastructure quality standards.
• Build knowledge-sharing culture with documentation and training.

Operational Excellence

• Manage and scale CI/CD pipelines integrated with Kubernetes.
• Implement security policies (RBAC, network policies, container scanning).
• Optimize cluster performance and cost-efficiency.
• Automate operations to minimize manual interventions.
• Ensure 99.9% uptime for production workloads.

Strategic Planning

• Define the infrastructure roadmap aligned with business needs.
• Evaluate and adopt new cloud-native technologies.
• Perform capacity planning and cloud cost optimization.
• Drive risk assessment and mitigation strategies.

🛠 Must-Have Technical Skills

Kubernetes Expertise

• 6+ years of hands-on Kubernetes experience in production.
• Deep knowledge of Kubernetes architecture (etcd, API server, scheduler, kubelet).
• Advanced Kubernetes networking (CNI, Ingress, Service mesh).
• Strong grasp of Kubernetes storage (CSI, PVs, StorageClasses).
• Experience with Operators and Custom Resource Definitions (CRDs).

Infrastructure as Code

• Terraform (advanced proficiency).
• Helm (developing and managing complex charts).
• Config management tools (Ansible, Chef, Puppet).
• GitOps workflows (ArgoCD, Flux).

Cloud Platforms

• Hands-on experience with at least 2 of the following:
• AWS: EKS, EC2, VPC, IAM, CloudFormation
• Azure: AKS, VNets, ARM templates
• GCP: GKE, Compute Engine, Deployment Manager

CI/CD & DevOps Tools

• Jenkins, GitLab CI, GitHub Actions, Azure DevOps
• Docker (advanced optimization and security practices)
• Container registries (ECR, ACR, GCR, Docker Hub)
• Strong Git workflows and branching strategies

Monitoring & Observability

• Prometheus & Grafana (metrics and dashboards)
• ELK/EFK stack (centralized logging)
• Jaeger/Zipkin (tracing)
• AlertManager (intelligent alerting)

💡 Good-to-Have Skills

• Service Mesh (Istio, Linkerd, Consul)
• Serverless (Knative, OpenFaaS, AWS Lambda)
• Running databases in Kubernetes (Postgres, MongoDB operators)
• ML pipelines (Kubeflow, MLflow)
• Security tools (Aqua, Twistlock, Falco, OPA)
• Compliance (SOC2, PCI-DSS, GDPR)
• Python/Go for automation
• Advanced Shell scripting (Bash/PowerShell)

🎓 Qualifications

• Bachelor’s in Computer Science, Engineering, or related field.
• Certifications (preferred):
• Certified Kubernetes Administrator (CKA)
• Certified Kubernetes Application Developer (CKAD)
• Cloud provider certifications (AWS/Azure/GCP).

Experience

• 6–7 years of DevOps/Infrastructure engineering.
• 4+ years of Kubernetes in production.
• 2+ years in a lead role managing teams.
• Experience with large-scale distributed systems and microservices.

Role Overview

We're looking for experienced Data Engineers who can independently design, build, and manage scalable data platforms. You'll work directly with clients and internal teams to develop robust data pipelines that support analytics, AI/ML, and operational systems.

You’ll also play a mentorship role and help establish strong engineering practices across our data projects.

Key Responsibilities

• Design and develop large-scale, distributed data pipelines (batch and streaming)
• Implement scalable data models, warehouses/lakehouses, and data lakes
• Translate business requirements into technical data solutions
• Optimize data pipelines for performance and reliability
• Ensure code is clean, modular, tested, and documented
• Contribute to architecture, tooling decisions, and platform setup
• Review code/design and mentor junior engineers

Must-Have Skills

• Strong programming skills in Python and advanced SQL
• Solid grasp of ETL/ELT, data modeling (OLTP & OLAP), and stream processing
• Hands-on experience with frameworks like Apache Spark, Flink, etc.
• Experience with orchestration tools like Airflow
• Familiarity with CI/CD pipelines and Git
• Ability to debug and scale data pipelines in production

Preferred Skills

• Experience with cloud platforms (AWS preferred, GCP or Azure also fine)
• Exposure to Databricks, dbt, or similar tools
• Understanding of data governance, quality frameworks, and observability
• Certifications (e.g., AWS Data Analytics, Solutions Architect, Databricks) are a bonus

What We’re Looking For

• Problem-solver with strong analytical skills and attention to detail
• Fast learner who can adapt across tools, tech stacks, and domains
• Comfortable working in fast-paced, client-facing environments
• Willingness to travel within India when required