Lead Software Engineer

Overview

Lead Software Engineer (AVP) role in MUFG's Bengaluru technology group, applying hands-on data integration, database engineering, scripting, cloud data services and AI-enabled solutions, including Generative AI capabilities such as retrieval-augmented generation (RAG), enterprise knowledge bases, semantic search and document intelligence.

What you'll do

• Design, develop, enhance and maintain complex ETL mappings, workflows, data pipelines and UNIX/Linux shell scripts in enterprise data warehouse and data lake environments.
• Build and optimize batch, real-time and near-real-time integrations using IDMC or comparable integration technologies.
• Develop advanced SQL and database solutions across SQL Server, Oracle, PostgreSQL or comparable relational platforms, including performance tuning and data-quality controls.
• Contribute to dimensional, canonical and integration data models, and support data warehouses and data marts.
• Build reusable Python, Java or scripting components for data processing, orchestration, validation, automation and operational support.
• Design and build enterprise RAG solutions that retrieve trusted content and provide grounded responses using large language models.
• Create and maintain knowledge ingestion pipelines covering document parsing, chunking, metadata enrichment, embedding generation, indexing, retrieval and citation or source traceability.
• Work with vector databases and semantic-search technologies such as Amazon OpenSearch, PostgreSQL with pgvector or equivalent platforms.
• Apply prompt engineering, hybrid search, reranking, context management and retrieval evaluation techniques to improve answer relevance and reduce unsupported responses.
• Build AI-enabled document intelligence solutions for extracting, classifying, summarizing and validating information from structured and unstructured content.
• Establish evaluation approaches for RAG and Generative AI solutions, including retrieval quality, answer groundedness, accuracy, latency, cost and regression testing.
• Implement responsible AI and model-risk controls, including data privacy, access control, content safety, human review, auditability, monitoring and secure handling of confidential information.
• Partner with architecture, security, data governance, model risk, legal/compliance and business teams to move AI proofs of concept into supportable production services.
• Design and support cloud-based data and AI solutions using relevant AWS services such as S3, RDS/Aurora, Redshift, Glue, Athena, Lambda, Step Functions, EventBridge, SQS, Kinesis, EMR, Textract, Bedrock, SageMaker, OpenSearch, IAM, KMS, CloudWatch and Secrets Manager.
• Design and deliver conversational "chat with data" solutions for banking use cases, enabling users to query governed structured and unstructured data through natural-language interfaces with appropriate access controls, source traceability, validation and safeguards against unsupported responses.
• Build resilient, observable and cost-conscious architectures using infrastructure-as-code, CI/CD, automated testing, logging, metrics and alerting.
• Apply secure cloud engineering patterns for network connectivity, encryption, secrets, least-privilege access, service integration and operational recovery.
• Provide technical knowledge and understanding across design, development, testing, implementation, release and production-support activities.
• Create and review functional designs, detailed technical designs, data mappings, interface specifications, runbooks and operational documentation.
• Gather and document business requirements, assess current technology and processes, and recommend practical target-state solutions.
• Coordinate with IT leads, developers, testers, operations and business stakeholders to ensure deliverables meet functional, non-functional, security and control expectations.
• Participate in production rollouts using change-control processes, clear documentation, stakeholder communication and implementation validation.
• Resolve production defects and incidents within defined service-level expectations; lead root-cause analysis and preventive remediation.
• Mentor engineers, promote engineering standards, conduct design and code reviews, and encourage reusable patterns and continuous improvement.

What you'll need

• Bachelor's Degree (or foreign equivalent degree) in Information Technology, Information Systems, Computer Science, Software Engineering or a related field.
• 10-15 years of hands-on experience in data engineering, ETL, databases, SQL and UNIX/Linux environments.
• 8+ years of data integration experience in a lead or senior engineering role, preferably within banking or financial services.
• 6+ years of hands-on experience working as a Data Engineer, designing, developing and supporting scalable data pipelines and data-processing solutions using AWS services.
• 8+ years of experience with IDMC or a comparable enterprise ETL/data-integration platform, including batch and real-time or near-real-time patterns.
• 5+ years of hands-on programming or scripting experience using Python, Java or a comparable language.
• Strong experience with one or more relational database platforms and advanced SQL, including performance tuning and troubleshooting.
• Working knowledge of AWS data, integration, security and serverless services.
• Demonstrated understanding of Generative AI concepts, embeddings, vector search, RAG architecture, knowledge bases, prompt engineering and LLM integration patterns.
• Experience producing technical designs and leading delivery through development, testing, release and production support.
• Hands-on experience delivering a RAG, semantic-search, conversational AI or enterprise knowledge-base solution from proof of concept through production.
• Experience with Amazon Bedrock knowledge bases or agents, Amazon SageMaker, Amazon OpenSearch, PostgreSQL/pgvector, LangChain, LlamaIndex or comparable frameworks and platforms.
• Experience with document-processing services and libraries, including OCR, layout-aware extraction, chunking, metadata management and source citation.
• Knowledge of LLMOps/MLOps practices, including prompt and model versioning, automated evaluation, observability, feedback loops, performance monitoring and cost controls.
• Understanding of data governance, AI governance, model risk, privacy, security and regulatory expectations relevant to financial services.
• Experience with ER/Studio, JIRA, enterprise schedulers such as Autosys, and Agile/Waterfall delivery methods.
• Experience with Big Data and streaming technologies such as Spark, Kafka, Hadoop, Hive, Kinesis or EMR.
• Experience with Terraform or another infrastructure-as-code tool and CI/CD pipelines.
• Strong communication, facilitation, analytical and stakeholder-management skills.

Nice to have

• Experience in the financial services or banking industry.

Details

• Location: MGS - Bengaluru.
• Reports to: VP.
• Corporate title: AVP.