• Provide overall direction, oversight, and coaching for a team of entry-level to mid-level software engineers that work on basic to moderately complex tasks.
• Accountable for decisions that influence teams’ resources, budget, tactical operations, and the execution and implementation of processes and procedures.
• Sets and scales operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams to improve delivery speed, quality, and operational outcomes; establishes measurable expectations (e.g., throughput, defect reduction, reliability) and ensures consistent validation, security, resiliency, and reuse of proven patterns. patterns.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive efficiency and support capacity unlock initiatives across teams, prioritizing reuse of existing firm technology assets. patterns.
• Ensures successful collaboration across teams and stakeholders.
• Identifies and mitigates issues to execute a book of work while escalating issues as necessary.
• Provides input to leadership regarding budget, approach, and technical considerations to improve operational efficiencies and functionality for the team.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience. In addition, 2 + years of experience leading technologists to manage and solve complex technical items within your domain of expertise.
• Ability to guide and coach teams on approach to achieve goals aligned against a set of strategic initiatives.
• Experience leading multi-team adoption of enterprise-authorized AI-assisted development and delivery tools, including defining governance/ways of working (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs. patterns.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and control expectations; ability to coach managers/leads and influence leaders on safe scaling patterns.
• Experience with hiring, developing, and recognizing talent.
• In-depth knowledge of the services industry and their IT systems.
• Practical cloud native experience.
• Experience working at code level.

Preferred Qualifications, Capabilities, and Skills

• Experience building data-driven products (analytics, insights platforms, dashboards, experimentation).

• Experience with machine learning frameworks/APIs and designing/implementing RAG pipelines or agentic AI workflows.

• Familiarity with AWS networking (VPC, subnets, routing, security groups), Terraform-based infrastructure deployments, and cost optimization strategies (autoscaling, spot vs. on-demand, auto-termination).

• Experience with RDBMS, NoSQL and Vector Databases and modernizing legacy systems while managing tech debt effectively.

• Background partnering closely with Product/Design, with demonstrated ability to establish UX standards and champion customer-centric, accessible design practices across engineering teams.

• Observability experience for data systems (freshness/completeness metrics, lineage, SLAs, alerting) and Databricks governance (Unity Catalog, catalogs/grants, auditing).

• Familiarity with the financial services industry.