Manager – AI/ML

RTX

. Lead and grow a team of 15–20 engineers across AI/ML engineering, software engineering, data engineering and domain-facing solution engineering .

Posted 9/18/2026full-timeBengaluru • IndiaSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in leading engineering teams, particularly in AI/ML and software engineering, with a strong focus on delivering production-ready systems. Proficient in managing performance, career development, and cross-functional collaboration while ensuring compliance with safety-critical standards.

Highest-signal resume keywords

AI/ML Systems DeliveryTeam ManagementMLOpsCloud DeploymentSafety-Critical Engineering

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI/ML EngineeringSoftware EngineeringData EngineeringModern ML SystemsEvaluation MethodologyCloud DeploymentMLOpsStage-Gated ResearchPerformance ManagementTechnical Direction

Soft Skills

LeadershipCollaborationCommunicationProblem-SolvingNegotiation

Tools & Technologies

CADFEA/CFDSimulationPLMRequirements Management

Industry Keywords

AerospaceDefenceAutomotiveSafety-Critical EngineeringRegulated Engineering

Tech Stack

Tools & technologies

CloudOpen Source

About the role

Key responsibilities & impact

Lead and grow a team of 15–20 engineers across AI/ML engineering, software engineering, data engineering and domain-facing solution engineering
Set technical direction and allocate people across concurrent AI proof-of-concepts
Own hiring, onboarding, performance management, career development and retention
Operate intake with Business Units and convert problems into testable hypotheses with success criteria
Run 8–12 week PoC cycles with stage gates, kill criteria and named BU sponsors
Maintain portfolio visibility into work in flight, costs, expected returns and stopped initiatives
Ensure each PoC produces a defensible evaluation with baseline comparisons, measured performance, failure-mode analysis and testing limitations
Recommend transition or scale paths based on production readiness, data dependencies, integration, run cost and ownership
Package successful PoCs for handover with architecture documentation, model cards, evaluation results, data lineage, limitations and backlog
Scale capabilities in-house through CI/CD, MLOps, monitoring, model lifecycle management, user support and product roadmaps
Negotiate ownership, funding and SLA boundaries with DT and BU engineering leadership
Set engineering standards for code quality, reproducibility, experiment tracking, data handling, evaluation and documentation
Ensure AI usage meets safety-critical assurance obligations, including traceability, human-in-the-loop controls and applicable standards
Work with export control, information security, legal and IP functions on data classification, model/tool selection and vendor risk
Represent the team to BU engineering leadership and report portfolio status, spend and realised benefit
Monitor vendors, open source, academia and industry consortia to inform build-versus-buy decisions

Requirements

What you’ll need

10+ years in software, data or AI engineering
4+ years managing engineers, including experience managing managers or pod/tech leads at a team size of 15 or above
Demonstrated delivery of AI/ML systems into production, not only prototypes
Hands-on technical depth in modern ML and LLM-based systems, RAG, agentic patterns, evaluation methodology, MLOps and cloud deployment
Experience working directly with engineering domain users in design, analysis, test, manufacturing or certification
Experience operating in a stage-gated research or innovation portfolio, including stop/continue decisions
Ability to hand over or scale capabilities across organizational boundaries
Aerospace, defence, automotive or another regulated, safety-critical engineering domain preferred
Familiarity with CAD, FEA/CFD, simulation, PLM and requirements management preferred
Exposure to AI assurance, explainability, neuro-symbolic or hybrid approaches preferred
Experience with cloud and edge/on-premise deployment, including constrained or disconnected environments preferred
Background in mechanical, systems or aerospace engineering preferred
Prior experience building a function from a small team to a scaled one preferred
All India positions require a background check, which may include a drug screen

Benefits

Comp & perks

Transportation facility
Group Term Life Insurance
Group Health Insurance
Group Personal Accident Insurance
18 days of vacation annually
12 days of contingency leave annually
Employee scholar program
Work life balance
Car lease program
National Pension Scheme
LTA
Fuel & Maintenance /Driver wages