Do you want your voice heard and your actions to count?

Discover your opportunity with Mitsubishi UFJ Financial Group (MUFG), one of the world’s leading financial groups. Across the globe, we’re 150,000 colleagues, striving to make a difference for every client, organization, and community we serve. We stand for our values, building long-term relationships, serving society, and fostering shared and sustainable growth for a better world.

With a vision to be the world’s most trusted financial group, it’s part of our culture to put people first, listen to new and diverse ideas and collaborate toward greater innovation, speed and agility. This means investing in talent, technologies, and tools that empower you to own your career.

Join MUFG, where being inspired is expected and making a meaningful impact is rewarded.

About the Role

Position Title   : Lead Software Engineer

Corporate Title: AVP (Internal: AVP)           

Reporting to    : VP

Location: MGS - Bengaluru                         

Job Profile

Position details

The AVP, Lead Software Engineer will apply hands-on technical knowledge across data integration, database engineering, scripting, cloud-based data services, and AI-enabled solutions. The role is to help design secure, scalable Generative AI capabilities, including retrieval-augmented generation (RAG), enterprise knowledge bases, semantic search, and document intelligence solutions.

The successful candidate will combine strong engineering fundamentals with analytical thinking, iterative problem-solving, stakeholder management, and a risk-aware approach. The individual will translate business and technical requirements into reliable solutions, guide development teams, and deliver against milestones in a regulated financial-services environment. In the process, you will have exciting opportunities to develop your skills, expand your network, and build your career.

Roles and Responsibilities

·       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 understandings 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

Job Requirements:

Bachelors’ Degree (or foreign equivalent degree) in Information Technology, Information Systems, Computer Science, Software Engineering, or a related field. Experience in the financial services or banking industry is preferred.

Minimum Qualifications:

·       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

Mitsubishi UFJ Financial Group (MUFG) is an equal opportunity employer. We view our employees as our key assets as they are fundamental to our long-term growth and success. MUFG is committed to hiring based on merit and organsational fit, regardless of race, religion or gender.