Software Engineer – AI & Automation - Senior Manager

AI Engineering & Orchestration Own the design and Implementation of scalable, multi‑step reasoning and agent‑based workflows, using the firm’s AI frame work to build platform services, pipelines & developer tooling. Collaborate with central AI team to translate framework releases into stable platform capabilities, working across platform teams, and third-party vendors to deliver production-grade solutions.

Design workflows incorporating: Retrieval‑augmented generation (RAG) across structured and unstructured data Implementing guardrails, validation layers, and hallucination‑mitigation techniques aligned standards defined in the firm. Translate business problems into impactful solutions, that incorporate automation across tools & workflows.

Serve as the primary code-level reviewer for the AI systems and frameworks. Define and enforce technology roadmaps, best practices, strategies, and standards for AI adoption across the organization. Contribute to the effort to commoditizing AI by incorporating it into existing systems (front-end & back-end apps).

Ensure solution quality across performance, scalability, security, cost efficiency, and responsible AI considerations. Mentor and coach junior engineers on advanced AI techniques, best practices, and design. Automation Engineering Build & maintain the infrastructure & APIs, that let the team adopt the team with minimal friction Lead delivery of production‑ready AI systems, including model deployment, APIs, orchestration pipelines, and enterprise integrations Establish and mature LLMOps practices, including versioning, monitoring, evaluation, logging, rollback strategies, and cost controls Ensure platforms meet enterprise standards for availability, scalability, performance, resilience, and reliability Translate business challenges into high‑impact AI use cases Guide experimentation and proof‑of‑concepts while ensuring a clear path to production Stay current on emerging AI technologies and recommend pragmatic adoption strategies Core/Must have skills Proficiency in Python , with strong experience in machine learning frameworks such as TensorFlow, PyTorch, and familiarity with data manipulation libraries (Pandas, NumPy) Experience working with Spec-driven engineering (SDD) and structured problem decomposition for AI-led development Ability to translate business requirements into agent workflows, tools, evaluation metrics, and deployable architectures Hands-on exposure to Model Context Protocol (MCP) and AI orchestration layers connecting models, APIs, tools, and enterprise systems Proficiency in developing LLM-based RAG (Retrieval Augmented Generation) Strong debugging and problem-solving skills across data, model, prompt, and orchestration layers Ability to communicate complex AI concepts through live demos, prototypes, and walkthroughs with both technical and non-technical stakeholders.

Demonstrated ability to independently design and build working AI prototypes and demos, not just conceptual architectures Excellent communication and interpersonal skills, with the ability to effectively interact with technical and non-technical stakeholders.

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