Data Scientist I B - GBS IND

Job Description:

About Us

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. Responsible Growth is how we run our company and how we deliver for our clients, teammates, communities and shareholders every day.

One of the keys to driving Responsible Growth is being a great place to work for our teammates around the world. We’re devoted to being a diverse and inclusive workplace for everyone. We hire individuals with a broad range of backgrounds and experiences and invest heavily in our teammates and their families by offering competitive benefits to support their physical, emotional, and financial well-being.

Bank of America believes both in the importance of working together and offering flexibility to our employees. We use a multi-faceted approach for flexibility, depending on the various roles in our organization. Working at Bank of America will give you a great career with opportunities to learn, grow and make an impact, along with the power to make a difference. Join us!

Global Business Services

Global Business Services delivers Technology and Operations capabilities to Lines of Business and Staff Support Functions of Bank of America through a centrally managed, globally integrated delivery model and globally resilient operations.

Global Business Services is recognized for flawless execution, sound risk management, operational resiliency, operational excellence and innovation.

In India, we are present in five locations and operate as BA Continuum India Private Limited (BACI), a non-banking subsidiary of Bank of America Corporation and the operating company for India operations of Global Business Services.

Process Overview

As part of Global Markets Technology Strategy & Innovation, working closely with the Quantitative Strategies & Data Group (QSDG) and Platform teams, this organization is chartered with driving the firms AI-first transformation across trading, research, and client engagement.

Our mission is to build enterprise-grade GenAI capabilities that unlock new insights from structured and unstructured data, accelerate revenue growth, and enhance operational efficiency across Global Markets. We are building a platform that serves a rapidly growing pipeline of GenAI use cases, leveraging both proprietary and open-source Large Language Models (LLMs) alongside the firms vast data assets.

Job Description

Generative AI (GenAI) presents a transformational opportunity to derive deeper insights from data, accelerate revenue growth, and enhance business processes. We are seeking a Senior Software Engineer to design, build, and deploy production-grade GenAI solutions on our Global Markets platform.

As a Senior Software Engineer, you will be hands-on building RAG pipelines, integrating LLMs, developing model serving infrastructure, and implementing MLOps workflows. You will work across the full stack—from data ingestion and embeddings to model deployment and monitoring—delivering AI-powered capabilities that directly impact traders, researchers, and clients.

This role requires strong software engineering fundamentals combined with deep expertise in GenAI technologies. You will collaborate with cross-functional teams, mentor junior engineers, and drive innovation through rapid prototyping and production delivery.

Responsibilities:-

GenAI Development & Implementation

• Design and implement end-to-end Retrieval-Augmented Generation (RAG) pipelines including document processing, embedding generation, vector storage, and retrieval optimization
• Build and deploy LLM-based applications using frameworks like LangChain, LlamaIndex, or Haystack
• Develop prompt engineering workflows, template libraries, and prompt optimization strategies for enterprise use cases
• Implement fine-tuning pipelines using LoRA, QLoRA, Adapters, or instruction tuning methodologies
• Build agent-based architectures with tool augmentation and multi-step reasoning capabilities
• Integrate multimodal models handling text, tabular data, and unstructured content
  
  

• Develop production-grade model serving infrastructure using Docker, Kubernetes/OpenShift, and model frameworks
• Build scalable inference APIs with load balancing, caching, and request routing
• Implement vector database solutions with optimized indexing and search
• Design and deploy microservices for data preprocessing, feature extraction, and post-processing
• Optimize model inference performance through quantization, batching, and hardware acceleration
  
  

• Build CI/CD pipelines for model deployment, versioning, and rollback
• Implement model monitoring, drift detection, and automated evaluation frameworks
• Develop A/B testing infrastructure for model comparison and champion/challenger strategies
• Create logging and observability solutions for LLM requests, responses, and performance metrics
• Build hallucination detection, factuality checks, and safety guardrails into production pipelines
  
  

• Partner with business stakeholders, quants, and product managers to translate requirements into technical solutions
• Conduct code reviews and provide technical mentorship to junior engineers
• Participate in architecture discussions and contribute to platform design decisions
• Drive agile delivery with sprint planning, estimation, and on-time execution
• Document technical designs, APIs, and operational runbooks
  
  

• Research and prototype emerging GenAI technologies and techniques
• Conduct performance benchmarking and optimization of AI workloads
• Implement comprehensive testing strategies including unit, integration, and evaluation tests
• Address security, compliance, and data privacy requirements across AI workflows
• Stay current with advances in LLMs, embeddings, vector search, and AI infrastructure
  
  

• Strong hands-on experience with LLM frameworks (Hugging Face Transformers, PyTorch, TensorFlow)
• Proven experience building and deploying RAG systems with vector databases
• Expert-level Python programming with experience in AI/ML libraries (LangChain, FAISS, sentence-transformers)
• Hands-on prompt engineering and building production prompt templates
• Experience deploying and scaling GenAI workloads in containerized environments
• Knowledge of LLM evaluation techniques (BLEU, ROUGE, BERTScore, human evaluation)
• Understanding of LLM safety frameworks, bias detection, and guardrail implementation
• Experience with embedding models and semantic search optimization
  
  

• Software engineering experience with strong full-stack capabilities
• Experience in building and deploying AI/ML solutions in production environments
• Expert-level Python programming and Unix/Linux scripting
• Experience with modern software engineering practices (Git, CI/CD, testing frameworks, code quality tools)
• Strong knowledge of relational databases (Oracle, MySQL, Impala) and NoSQL systems (MongoDB, Redis)
• Experience with front-end technologies (HTML/CSS, JavaScript, React) and RESTful API development
• Proficiency with cloud platforms and container orchestration (Kubernetes, Docker)
• Strong problem-solving skills and ability to debug complex distributed systems
• Excellent communication skills for technical and business audiences
  
  

• Experience implementing AI solutions in financial services or regulated environments
• Knowledge of LLM fine-tuning, model compression, or inference optimization
• Hands-on experience with agent frameworks and tool-augmented LLMs
• Exposure to Global Markets (Sales & Trading) workflows and financial data
• Experience with real-time data processing and streaming architectures (Kafka)
• Contributions to open-source AI/ML projects