Data Scientist developing and implementing LLM - based agents and leveraging AI techniques to improve client value. Collaborating on project challenges in a dynamic, start - up environment at Gartner.
About the role
- Senior Software Engineer designing, building, and deploying production-grade GenAI solutions at Bank of America. Collaborating across teams to deliver AI-powered capabilities impacting traders and clients.
Responsibilities
- 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
Requirements
- Bachelor's or master’s degree in computer science, Engineering, or equivalent experience
- 06-10+ years of experience
- 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
Benefits
- competitive benefits to support their physical, emotional, and financial well-being
Job title
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