As
a Senior AI/ML Engineer, you will own end-to-end delivery of AI/ML solutions
for the banking platform platform, from problem definition and data enablement
through model development, deployment, and ongoing operations. You will design,
build, deploy, and support agentic and ML-driven capabilities integrated into
platform workflows, CI/CD pipelines, and operational tooling.
You
will apply production-grade engineering practices across the model lifecycle,
including training, evaluation, deployment, monitoring, drift management, and
measurable outcome tracking. You will build secure-by-design solutions suitable
for an enterprise environment and collaborate closely with internal platform
teams and downstream internal consumers to ensure solutions are reliable,
auditable where needed, and operationally effective.
Key Responsibilities
·Design,
develop, and deploy AI/ML models and agent-based systems that automate
technology and platform workflows for the internal banking platform platform.
·Lead
the integration of intelligent agents into operational processes to improve
decision-making, workflow execution, and process optimization across
engineering and operations.
·Build
AI-assisted tooling that improves Infrastructure as Code (IaC) development,
validation, and change management (examples: Terraform, Ansible, CloudFormation
style patterns) across cloud and on-prem environments.
·Partner
with DevOps and platform engineering teams to enhance CI/CD pipelines using
AI/ML for signal detection, predictive analytics, and automated remediation.
·Develop
AI-powered observability automation to monitor, analyze, and proactively manage
application and infrastructure health for internal platform services.
·Automate
alert triage, root cause analysis assistance, and incident response workflows
using ML-driven techniques, with clear guardrails and measurable outcomes.
·Engineer
or enable data pipelines and feature workflows needed to support model
training, evaluation, and real-time or near-real-time inference use cases.
·Implement
and operate MLOps capabilities (deployment patterns, monitoring, quality gates,
rollback strategies, documentation) aligned to enterprise expectations for
reliability and risk management.
·Collaborate
with cross-functional teams (engineering, product, SRE, operations,
architecture, controls) to identify high-value automation opportunities and
deliver outcomes that can be adopted at scale.
·Continuously
evaluate emerging AI/ML approaches and tooling, and translate them into
practical, secure, and maintainable platform capabilities.
Required Qualifications (Minimum)
·Bachelor’s
or Master’s degree in Computer Science, Engineering, or related field, or
equivalent practical experience.
·5+
years total engineering experience with 3+ years hands-on experience delivering
AI/ML engineering solutions in production environments.
·Strong
programming skills in Python; Java experience is a plus, especially for
enterprise platform integration.
·Experience
with ML frameworks such as PyTorch, TensorFlow, and scikit-learn, including
model training and evaluation workflows.
·Hands-on
experience building agent-based systems and integrating them into real
operational or engineering workflows.
·Experience
applying AI/ML to automate or improve Infrastructure as Code workflows (examples:
generation assistance, validation, policy checks, drift detection, change risk
scoring).
·Familiarity
with observability fundamentals and toolsets (examples: Prometheus, Grafana,
ELK stack) and experience automating operational workflows using AI/ML.
·Strong
foundations in data structures, algorithms, machine learning, statistics, and
software engineering best practices.
·Experience
integrating AI capabilities into modern software development practices and
supporting legacy modernization or code transformation initiatives.
·Strong
communication and collaboration skills, with the ability to work effectively
across engineering and operations stakeholders.
Preferred Qualifications
·Depth
in one or more areas such as large language models, NLP, knowledge graphs,
reinforcement learning, ranking and recommendation, or time-series analysis.
·Experience
with retrieval-augmented generation patterns or tool-using agents, including
multi-step workflows and structured evaluation approaches.
·Experience
with MLOps practices and concepts such as model registries, feature store
patterns, CI/CD for ML, and model monitoring and drift detection.
·Experience
building production systems on public cloud platforms (examples: AWS, Azure,
GCP) and operating containerized workloads (examples: Docker, Kubernetes).
·Experience
operating ML solutions in regulated enterprise environments, including
producing controls-oriented documentation and supporting auditability
expectations.
·Experience
with platform-scale operational automation, including incident response
automation and measurable reductions in operational toil.
Information
Locations Position Open to Only localsIndustry Information TechnologyStatus OpenJob Age 19 Day'sCreated Date 08/07/2026No.of Positions 1Duration 12+ monthsZip Code
