Senior Engineer providing technical leadership for AI-powered applications at Collins Aerospace. Leading projects from conception to production with a focus on AI, full-stack development, and emerging technologies.
Responsibilities
Lead end-to-end delivery of AI-powered applications, from problem framing and system architecture to development, deployment, and operationalization.
Design and build agentic AI systems, AI copilots, and intelligent automation platforms that integrate deeply into engineering workflows.
Own production readiness: scalability, reliability, security, monitoring, and lifecycle management of AI-enabled systems.
Architect full-stack solutions including APIs, backend services, data pipelines, and user-facing interfaces for AI-driven platforms.
Guide teams in moving beyond demos by establishing deployment patterns, MLOps pipelines, and reusable platform components.
Collaborate with global stakeholders to drive alignment, manage risks, and deliver measurable outcomes.
Evaluate emerging AI technologies and frameworks, translating them into practical, deployable solutions, not just experiments.
Mentor engineers and set technical standards through reviews, architecture forums, and hands-on contribution.
Requirements
M.Tech with at least 7 years of experience or PhD with 3+ years of experience.
At least 2 years leading or owning delivery of AI/ML-powered systems.
6 years of software engineering experience, with a strong full-stack foundation and increasing ownership of complex systems.
Proven experience building AI-enabled applications beyond notebooks—systems that are deployed, used, and maintained.
Hands-on experience with agentic AI frameworks, LLM-based systems, or AI copilots, including orchestration, tool use, and guardrails.
Strong proficiency in Python plus at least one additional language (Java, C++, C#, or similar).
Experience designing scalable backend architectures, APIs, and services supporting AI workloads.
MLOps experience: model deployment, CI/CD for AI systems, monitoring, versioning, and lifecycle management.
Working knowledge across NLP, Computer Vision, or Data Analytics, with depth in at least one area.
Comfort operating in regulated, safety-conscious, or compliance-driven environments (aerospace experience is a plus, not a requirement).
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