Data Scientist

Data Engineer MS Data Engineer + Snowflake/databrics Required Skills: · 6 to 8 years of being a practitioner in data engineering or a related field. Should have experience in Snowflake or Databricks. Experience with data processing frameworks like Apache Spark or Hadoop. Experience working on Databricks. Familiarity with cloud platforms (AWS, Azure) and their data services. Experience with data warehousing concepts and technologies. Experience with message queues and streaming platforms (e.g., Kafka). Excellent communication and collaboration skills. Ability to work independently and as part of a geographically distributed team. 

The Role

As a DevOps Engineer at Blitzy's Pune headquarters, you'll build and operate the infrastructure that powers our AI agents and the applications they produce. You'll work at the intersection of cloud infrastructure, developer tooling, and AI-native systems — designing the pipelines, clusters, and automation that allow Blitzy to ship production-ready software at machine speed. This is a hands-on, high-ownership role for an engineer who moves fast, automates everything, and cares deeply about developer experience and system reliability.

What Success Looks Like

• Kubernetes clusters are running reliably at scale, with clear deployment standards, Helm-managed releases, and minimal manual intervention required from engineering teams.
• CI/CD pipelines are fast, consistent, and trusted — developers ship confidently knowing the automation handles the rest.
• Observability is comprehensive: alerts are actionable, dashboards are meaningful, and incidents are resolved faster because the right data is always available.
• Infrastructure provisioning is fully automated — no snowflake environments, no manual setup, everything reproducible through code.
• AI agent orchestration infrastructure is stable and scalable, directly enabling Blitzy's core product to deliver for enterprise customers.
• Engineering teams notice the difference — developer productivity is measurably higher and infrastructure is no longer a bottleneck to shipping.

Areas of Ownership

• Build and manage Kubernetes clusters supporting AI agent workloads and application deployment at scale.
• Design, implement, and maintain CI/CD pipelines for application and AI service delivery — ensuring speed, reliability, and repeatability.
• Automate infrastructure provisioning and dynamic scaling using Python scripts and Terraform IaC.
• Deploy and manage applications using Helm charts; own packaging standards and release automation.
• Build and maintain comprehensive observability stacks — alerting, distributed tracing, metrics, and logging (e.g., Prometheus, Grafana, Datadog, OpenTelemetry).
• Monitor and maintain production services and APIs; own incident response and drive blameless postmortems.
• Build dedicated infrastructure for AI agent orchestration and management, enabling Blitzy's core autonomous development capabilities.
• Collaborate with engineering teams on deployment strategies and continuously improve developer experience through tooling and automation.

Required Experience

• 5–8 years of DevOps, infrastructure, or platform engineering experience.
• Python proficiency for scripting, automation, and infrastructure tooling.
• Deep Kubernetes expertise — cluster management, workload deployment, scaling, and troubleshooting.
• Hands-on Helm experience for application packaging and release management.
• Proven ability to design and implement CI/CD pipelines across complex, multi-service environments.
• Practical experience with at least one major cloud platform (AWS, GCP, or Azure).
• Terraform proficiency for infrastructure-as-code provisioning and state management.
• Strong Linux administration and containerization fundamentals (Docker, OCI).

What Makes You Stand Out

• CKA (Certified Kubernetes Administrator) certification.
• Familiarity with MLOps tooling such as MLflow, Kubeflow, or similar platforms for AI/ML workload management.
• Experience with microservices architecture and distributed systems design.
• Knowledge of API gateways and service mesh technologies (Istio, Linkerd, or equivalent).
• Prior experience in a high-growth AI or software startup where you moved fast and owned broadly.
• Track record of meaningfully improving developer productivity through platform and tooling investments.

What Makes This Role Different

Most DevOps roles have you maintaining existing systems. At Blitzy, you're building the infrastructure layer for a platform that autonomously writes enterprise software — a genuinely new category of product. You'll work on AI agent orchestration, Kubernetes at scale, and developer tooling that is directly responsible for how fast Blitzy delivers value to Fortune 500 customers. As an early member of the Pune engineering team, you'll have outsized influence over our infrastructure culture and technical direction. High performers are eligible for company equity — giving you real ownership in what you build.

Strong Senior GenAI / AI Backend Engineer Profiles

Mandatory (Experience 1) – Must have 4+ years of total software development experience, with at least 2+ years working on AI/LLM-based features in production

Mandatory (Experience 2) – Must have strong backend engineering experience using Python (FastAPI / Django preferred) and building production-grade systems

Mandatory (Experience 3) – Must have hands-on experience building LLM-based applications, including OpenAI / Gemini / similar models in real projects

Mandatory (Experience 4) – Must have experience with RAG (Retrieval Augmented Generation) including chunking, embeddings, and retrieval pipelines

Mandatory (Experience 5) – Must have experience designing end-to-end AI pipelines, including chaining, tool usage, structured outputs, and handling failure cases

Mandatory (Experience 6) – Must have experience building agentic AI systems (multi-step workflows, tool orchestration like LangGraph / CrewAI or custom agents)

Mandatory (Experience 7) – Must have strong coding and system design skills, not just prompt engineering or experimentation

Mandatory (Experience 8) – Must have experience shipping AI features in production, not just POCs or research projects

Mandatory (Experience 9) – Must have experience working with APIs, backend services, and integrations

Mandatory (Experience 10) – Must have understanding of AI system reliability, including latency, cost optimization, fallback handling, and basic eval thinking

Mandatory (Company) – Product companies / startups, preferably Series A to Series D

Mandatory (Note) - Candidate's overall experience should not be more than 7 Yrs

Mandatory (Tech Stack) – Strong in Python + AI/LLM ecosystem, experience with modern AI tooling and frameworks

Mandatory (Exclusion) – Reject profiles that are only Prompt Engineers, Data Scientists, or Frontend Engineers without strong backend + system building experience

KEY DUTIES

• Independently own and resolve high-priority or complex customer issues with minimal supervision
• Reproduce and analyze product defects using advanced troubleshooting techniques and tools
• Collaborate with developers to identify root causes and drive timely resolution of defects
• Identify trends in escalations and provide feedback to improve product quality and customer experience
• Document investigation findings, root causes, and resolution steps clearly for both internal and external audiences
• Contribute to knowledge base articles and process improvements to enhance team efficiency
• Represent the escalation team in product reviews or defect triage meetings
• Build subject matter expertise in specific products or components
• Mentor and assist junior team members by reviewing their investigations and coaching through complex cases
• Participate in Agile ceremonies and contribute to team planning and backlog refinement
• Other duties as assigned

BASIC QUALIFICATIONS

• Typically requires 3–6 years of technical experience in a support, development, or escalation role
• Strong technical troubleshooting and root cause analysis skills
• Proficient in debugging tools, logs, and test environments
• Ability to independently manage multiple complex issues and drive them to closure
• Experience working with cross-functional teams in a collaborative, Agile environment
• Proficiency with relevant scripting or programming languages (e.g., Python, Bash, PowerShell, Java)
• Exceptional written and verbal communication skills — especially when engaging with customers in critical or escalated situations
• Demonstrated customer-first mindset with an emphasis on clarity, empathy, and follow- through
• Proactive and detail-oriented, with the ability to document and communicate technical concepts clearly
• Comfortable presenting findings or recommendations to both technical and non-technical stakeholders

Responsibilities

• Ideate, execute & take ownership of complete project from scratch
• Create and design User-focused focused SAAS with high end experience  
• Optimize existing architecture for performance, scalability & functionality 
• Generate modular & clean codes 
• Deploy & maintain project’s infrastructure with zero downtime 

Skill Sets

• Sharp communication skills 
• Fluent in Problem Solving, Data Structures And Algorithms
• Strong in Java Spring Boot & Python Django languages & frameworks
• Experience with database design & familiar with RDBMS database like Postgresql as well as no SQL databases 
• Good with writing unit test cases & integration test cases 
• Well versed in writing Asynchronous codes or technologies
• Exposed to various AWS technologies like EC2, Cognito, API Gateway,ECS etc.
• Exposed to Pub-sub technologies like SQS,Kafka, RabbitMQ, etc. 
• Familiar with caching tools like Redis, HazleCaste, etc. 
• Exposure to DevOps and Big Data is a plus.

• Research and develop statistical learning models for data analysis
• Collaborate with product management and engineering departments to understand company needs and devise possible solutions
• Keep up-to-date with latest technology trends
• Communicate results and ideas to key decision makers
• Implement new statistical or other mathematical methodologies as needed for specific models or analysis
• Optimize joint development efforts through appropriate database use and project design

Qualifications/Requirements:

• Masters or PhD in Computer Science, Electrical Engineering, Statistics, Applied Math or equivalent fields with strong mathematical background
• Excellent understanding of machine learning techniques and algorithms, including clustering, anomaly detection, optimization, neural network etc
• 3+ years experiences building data science-driven solutions including data collection, feature selection, model training, post-deployment validation
• Strong hands-on coding skills (preferably in Python) processing large-scale data set and developing machine learning models
• Familiar with one or more machine learning or statistical modeling tools such as Numpy, ScikitLearn, MLlib, Tensorflow
• Good team worker with excellent communication skills written, verbal and presentation

Desired Experience:

• Experience with AWS, S3, Flink, Spark, Kafka, Elastic Search
• Knowledge and experience with NLP technology
• Previous work in a start-up environment