Senior Software Engineer

Barclays

2 - 12 years

20 - 25 Lacs

pune

Posted:1 day ago| Platform:

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Skills Required

hive kubernetes scala aws iot core emr eks microservices sql route3 software development life cycle ecs spark devops hadoop amazon cloudwatch maven vpc engineering aws cloudformation amazon sqs aws glue amazon ec2 lambda expressions sns gitlab aws

Work Mode

Work from Office

Job Type

Full Time

Job Description

Technical Skills: Expertise in SPARK/Scala development through the full software development lifecycle (SDLC), including design, development, testing, and deployment.
ETL & Data Pipelines: Build and optimize ETL processes and data pipelines for large-scale data processing using Apache Spark, Scala, Hive, and Airflow.
DevOps & CI/CD: Implement and manage DevOps pipelines using tools like Gitlab, SBT, or Maven.
Code Quality & Best Practices: Conduct code reviews, enforce coding standards, and ensure test coverage and documentation.
Stakeholder Collaboration: Work closely with business analysts, product owners, and other stakeholders to help translate requirements into technical solutions.
Architecture & Design: Implement scalable architectures using Scala, Spark, and Hadoop ecosystems.

Essential Skills:

Languages & Frameworks: Scala (core), Spark, Hive, SQL, Snowflakes, AWS.
Tools: Apache Airflow, Gitlab, SBT, Maven.
Cloud & Microservices: Experience with cloud adoption and microservice architecture is mandatory.
Working knowledge of AWS.
Core AWS service - EC2, Lambda, ECS, EKS ( Kubernetes on AWS) .
Storage & database -S3, Document DB.
Networking -VPC, Route53, API Gateway, CloudFront.
Data Engineering & processing -> AWS Glue / Athena/ EMR.
Event processing -SNS, SQS, Event Bridge etc.
Security & IAM - Policies, roles, fine-grained access, KMS encryption etc.
DevOps -CloudFormation/CDK/Terraform, CloudWatch etc.
Experience of working in Gen AI & Agentic AI technologies.
Deep understanding of LLMs, SMLs, Embedding models, Classification models etc.
Proficient in Prompting techniques and prompt engineering.
Preferred expertise in vibe-coding.
Understanding of Agentic Architectures.
Hands-on experience of any of AI Agent Frameworks like Autogen, CrewAI etc.
Working experience on building LLM based apps on any cloud platforms like Amazon Bedrock, Google Vertex etc.
Hands-on working experience on LLM orchestration and RAG frameworks like Lang chain, Lang flow, Haystack etc.
Working experience on RAG based workflows and MCP.
Domain Knowledge: Familiarity with Risk, Finance, and Treasury systems is highly desirable.

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