Feature Lead - Technology, AI/ML

Bank of America•Plano, TX

1d•Onsite

About The Position

This job is responsible for providing leadership, technical direction and oversight to a team delivering technology solutions. Key responsibilities of the job are to provide oversight of the design, implementation, and maintenance of complex computer programs, align technical solutions to business objectives, and ensure that coding practices/quality comply with software development standards. Job expectations include conducting multiple software implementations and applying both depth and breadth in knowledge of several technical competencies. This is a Lead position in Bank of America’s Erica, Chat, and Voice Technology organization. The Team builds next generation AI and Search platforms to enable virtual financial assistant / AI and search capabilities across multiple channels.

Requirements

5+ years of hands-on software development experience using Java, Python, or related technologies.
Strong software engineering skills with proficiency in Python and Java.
Experience building AI/ML-powered applications using machine learning and Generative AI techniques.
Knowledge of modern AI technologies including Transformers, Large Language Models (LLMs), Agentic AI, and Generative AI frameworks.
Experience implementing Retrieval Augmented Generation (RAG) pipelines and related retrieval techniques.
Experience deploying and optimizing AI applications on CPU/GPU infrastructure using technologies such as CUDA, vLLM, Triton, or equivalent inference platforms.
Understanding of supervised, unsupervised, and reinforcement learning methodologies.
Good understanding of source control systems such as Git and modern collaborative development practices.
Good interpersonal communication skills for technical and business conversations
Good analytical skills to break down requirements and solve complex problems
Proven task management and leadership skills
Experience building restful web services
Bachelors or Master’s Degree in Computer Science or related field

Nice To Haves

Experience in performance tuning with good understanding of JVM internals
Experience with NoSQL databases like Cassandra
Experience in distributed caching frameworks like hazelcast, ignite, redis
Experience in modern JVM languages like groovy, scala
Experience with Full-stack development, especially including Angular
Experience with Generative AI Spring MVC
Experience with Container technologies, such as Kubernetes and Docker
Experience working with NLP and Machine learning
Prior open source contributions
Background in mathematics or statistics
Proven task management and leadership skills
Experience working in agile teams
Experience building chatbot, conversational AI, or virtual assistant platforms.
Experience with Azure, AWS, or cloud-native AI platforms.
Experience with vector databases, embeddings, semantic search, and AI retrieval systems.
Experience with Elasticsearch, SOLR, OpenSearch, or related search technologies.
Experience with MLOps, model deployment, monitoring, and AI observability.
Experience with inference optimization frameworks, GPU acceleration, and distributed model serving.
Experience with prompt engineering, model evaluation, and AI safety/governance practices.

Responsibilities

Designs, develops and is accountable for feature delivery
Applies enterprise standards for solution design, coding and quality
Ensures solution meets product acceptance criteria with minimal technical debt
Guides the team on work breakdown and execution
Works with the Product Owner to ensure that product backlog/requirements are healthy, with clear acceptance criteria
Plays a team lead role (as an individual contributor) and mentors the team
Guides team members with skills and practices (planning and estimation, peer reviews, and other engineering practices)
Guides the team on solution design, AI/ML model integration, work breakdown, and execution.
Evaluates and applies emerging AI technologies, frameworks, and models to solve business problems.
Leads the deployment and optimization of AI workloads across CPU/GPU infrastructures and cloud platforms.
Ensures AI solutions meet enterprise standards for security, governance, observability, performance, and responsible AI practices.
Guides the team in implementing prompt engineering, retrieval strategies, model evaluation, and inference optimization techniques.
Drives technical innovation and adoption of AI/ML best practices across the organization.